From 1cb8fcae92488659414e3a186760520bcec8cfc9 Mon Sep 17 00:00:00 2001 From: Norbert Date: Sun, 20 Aug 2017 17:43:17 +0200 Subject: [PATCH 01/83] Initial commit --- .gitignore | 101 +++++++++++++++++++++++++++++++++++++++++++++++++++++ README.md | 1 + 2 files changed, 102 insertions(+) create mode 100644 .gitignore create mode 100644 README.md diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..7bbc71c0 --- /dev/null +++ b/.gitignore @@ -0,0 +1,101 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ diff --git a/README.md b/README.md new file mode 100644 index 00000000..1c109d8d --- /dev/null +++ b/README.md @@ -0,0 +1 @@ +# openai-maze-envs \ No newline at end of file From 1be29d21975c091ae9f14438c85ecfb964a0575e Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Sun, 20 Aug 2017 21:41:29 +0200 Subject: [PATCH 02/83] Init commit --- .gitignore | 1 + ex.py | 25 +++++++ gym_maze/Maze.py | 109 ++++++++++++++++++++++++++++++ gym_maze/__init__.py | 14 ++++ gym_maze/envs/AbstractMaze.py | 124 ++++++++++++++++++++++++++++++++++ gym_maze/envs/Maze1.py | 15 ++++ gym_maze/envs/__init__.py | 2 + requirements.txt | 1 + setup.py | 6 ++ 9 files changed, 297 insertions(+) create mode 100644 ex.py create mode 100644 gym_maze/Maze.py create mode 100644 gym_maze/__init__.py create mode 100644 gym_maze/envs/AbstractMaze.py create mode 100644 gym_maze/envs/Maze1.py create mode 100644 gym_maze/envs/__init__.py create mode 100644 requirements.txt create mode 100644 setup.py diff --git a/.gitignore b/.gitignore index 7bbc71c0..64fe1bda 100644 --- a/.gitignore +++ b/.gitignore @@ -99,3 +99,4 @@ ENV/ # mypy .mypy_cache/ +.idea \ No newline at end of file diff --git a/ex.py b/ex.py new file mode 100644 index 00000000..aeaf61c3 --- /dev/null +++ b/ex.py @@ -0,0 +1,25 @@ +import logging + +import gym +import gym_maze + +logger = logging.getLogger() +logger.setLevel(logging.INFO) + +env = gym.make('Maze1-v0') + +for i_episode in range(1): + observation = env.reset() + + for t in range(100): + env.render() + print(observation) + action = env.action_space.sample() + observation, reward, done, info = env.step(action) + + if done: + print("Episode finished after {} timesteps".format(t + 1)) + break + + +logger.info("Finished") diff --git a/gym_maze/Maze.py b/gym_maze/Maze.py new file mode 100644 index 00000000..a0325399 --- /dev/null +++ b/gym_maze/Maze.py @@ -0,0 +1,109 @@ +import logging + +logger = logging.getLogger(__name__) + +PATH_MAPPING = 0 +WALL_MAPPING = 1 +REWARD_MAPPING = 9 + + +class Maze: + """ + Creates new maze. + + Mapping: + 0 - path + 1 - wall + 9 - reward + """ + + def __init__(self, matrix): + self.matrix = matrix + self.max_x = self.matrix.shape[1] + self.max_y = self.matrix.shape[0] + + def get_possible_insertion_coordinates(self): + """ + Returns a list with coordinates in the environment where + an agent can be placed (only on the path). + :return: list of tuples (X,Y) containing coordinates + """ + possible_cords = [] + for x in range(0, self.max_x): + for y in range(0, self.max_y): + if self.is_path(x, y): + possible_cords.append((x, y)) + + return possible_cords + + def perception(self, pos_x, pos_y): + if not self._within_x_range(pos_x): + raise ValueError('X position not within allowed range') + + if not self._within_y_range(pos_y): + raise ValueError('Y position not within allowed range') + + # Position N + if pos_y == 0: + n = None + else: + n = str(self.matrix[pos_y - 1, pos_x]) + + # Position NE + if pos_x == self.max_x - 1 or pos_y == 0: + ne = None + else: + ne = str(self.matrix[pos_y - 1, pos_x + 1]) + + # Position E + if pos_x == self.max_x - 1: + e = None + else: + e = str(self.matrix[pos_y, pos_x + 1]) + + # Position SE + if pos_x == self.max_x - 1 or pos_y == self.max_y - 1: + se = None + else: + se = str(self.matrix[pos_y + 1, pos_x + 1]) + + # Position S + if pos_y == (self.max_y - 1): + s = None + else: + s = str(self.matrix[pos_y + 1, pos_x]) + + # Position SW + if pos_x == 0 or pos_y == self.max_y - 1: + sw = None + else: + sw = str(self.matrix[pos_y + 1, pos_x - 1]) + + # Position W + if pos_x == 0: + w = None + else: + w = str(self.matrix[pos_y, pos_x - 1]) + + # Position NW + if pos_x == 0 or pos_y == 0: + nw = None + else: + nw = str(self.matrix[pos_y - 1, pos_x - 1]) + + return n, ne, e, se, s, sw, w, nw + + def is_wall(self, pos_x, pos_y): + return self.matrix[pos_y, pos_x] == WALL_MAPPING + + def is_path(self, pos_x, pos_y): + return self.matrix[pos_y, pos_x] == PATH_MAPPING + + def is_reward(self, pos_x, pos_y): + return self.matrix[pos_y, pos_x] == REWARD_MAPPING + + def _within_x_range(self, x): + return 0 <= x < self.max_x + + def _within_y_range(self, y): + return 0 <= y < self.max_y diff --git a/gym_maze/__init__.py b/gym_maze/__init__.py new file mode 100644 index 00000000..4b7eaa75 --- /dev/null +++ b/gym_maze/__init__.py @@ -0,0 +1,14 @@ +import logging + +from gym.envs.registration import register + +from gym_maze.Maze import Maze +from gym_maze.Maze import PATH_MAPPING, WALL_MAPPING, REWARD_MAPPING + +logger = logging.getLogger(__name__) + +register( + id='Maze1-v0', + entry_point='gym_maze.envs:Maze1', + nondeterministic=True +) diff --git a/gym_maze/envs/AbstractMaze.py b/gym_maze/envs/AbstractMaze.py new file mode 100644 index 00000000..9eab4f38 --- /dev/null +++ b/gym_maze/envs/AbstractMaze.py @@ -0,0 +1,124 @@ +import gym +from gym import error, spaces, utils +from gym.utils import seeding + +from gym_maze import Maze, WALL_MAPPING + +import numpy as np +import logging +import random + + +logger = logging.getLogger(__name__) + +ACTION_LOOKUP = { + 0: 'N', + 1: 'NE', + 2: 'E', + 3: 'SE', + 4: 'S', + 5: 'SW', + 6: 'W', + 7: 'NW' +} + + +class AbstractMaze(gym.Env): + metadata = {'render.modes': ['human']} + + def __init__(self, matrix): + self.maze = Maze(matrix) + + self.action_space = spaces.Discrete(8) + self.observation_space = spaces.Discrete(8) + + def _step(self, action): + previous_observation = self._observe() + logger.debug("Previous observation: [{}]".format(previous_observation)) + self._take_action(action, previous_observation) + + observation = self._observe() + reward = self._get_reward() + episode_over = self._is_over() + + return observation, reward, episode_over, {} + + def _reset(self): + logger.debug("Resetting the environment") + self._insert_animat() + return self._observe() + + def _render(self, mode='human', close=False): + logging.debug("Rendering the environment") + ANIMAT_MARKER = '5' + + situation = np.copy(self.maze.matrix) + situation[self.pos_y, self.pos_x] = ANIMAT_MARKER + + logger.info("\n{}".format(situation)) + + def _observe(self): + return self.maze.perception(self.pos_x, self.pos_y) + + def _get_reward(self): + if self.maze.is_reward(self.pos_x, self.pos_y): + return 1000 + + return 0 + + def _is_over(self): + return self.maze.is_reward(self.pos_x, self.pos_y) + + def _take_action(self, action, observation): + """Executes the action inside the maze""" + animat_moved = False + action_type = ACTION_LOOKUP[action] + + if action_type == "N" and not self.is_wall(observation[0]): + self.pos_y -= 1 + animat_moved = True + + if action_type == 'NE' and not self.is_wall(observation[1]): + self.pos_x += 1 + self.pos_y -= 1 + animat_moved = True + + if action_type == "E" and not self.is_wall(observation[2]): + self.pos_x += 1 + animat_moved = True + + if action_type == 'SE' and not self.is_wall(observation[3]): + self.pos_x += 1 + self.pos_y += 1 + animat_moved = True + + if action_type == "S" and not self.is_wall(observation[4]): + self.pos_y += 1 + animat_moved = True + + if action_type == 'SW' and not self.is_wall(observation[5]): + self.pos_x -= 1 + self.pos_y += 1 + animat_moved = True + + if action_type == "W" and not self.is_wall(observation[6]): + self.pos_x -= 1 + animat_moved = True + + if action_type == 'NW' and not self.is_wall(observation[7]): + self.pos_x -= 1 + self.pos_y -= 1 + animat_moved = True + + return animat_moved + + def _insert_animat(self): + possible_coords = self.maze.get_possible_insertion_coordinates() + + starting_position = random.choice(possible_coords) + self.pos_x = starting_position[0] + self.pos_y = starting_position[1] + + @staticmethod + def is_wall(perception): + return perception == str(WALL_MAPPING) diff --git a/gym_maze/envs/Maze1.py b/gym_maze/envs/Maze1.py new file mode 100644 index 00000000..bb89803d --- /dev/null +++ b/gym_maze/envs/Maze1.py @@ -0,0 +1,15 @@ +from gym_maze.envs import AbstractMaze + +import numpy as np + + +class Maze1(AbstractMaze): + def __init__(self): + super().__init__(np.matrix([ + [1, 1, 1, 1], + [1, 0, 9, 1], + [1, 0, 1, 1], + [1, 0, 0, 1], + [1, 0, 1, 1], + [1, 1, 1, 1], + ])) diff --git a/gym_maze/envs/__init__.py b/gym_maze/envs/__init__.py new file mode 100644 index 00000000..5af22c1e --- /dev/null +++ b/gym_maze/envs/__init__.py @@ -0,0 +1,2 @@ +from gym_maze.envs.AbstractMaze import AbstractMaze +from gym_maze.envs.Maze1 import Maze1 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..b7dba22a --- /dev/null +++ b/requirements.txt @@ -0,0 +1 @@ +gym==0.9.2 diff --git a/setup.py b/setup.py new file mode 100644 index 00000000..d00178e9 --- /dev/null +++ b/setup.py @@ -0,0 +1,6 @@ +from setuptools import setup + +setup(name='gym_maze', + version='0.0.1', + install_requires=['gym'] +) From d84899fa62fee1f248eb2d7cc07319f18f351cff Mon Sep 17 00:00:00 2001 From: "Norbert Kozlowski (PGS Software)" Date: Mon, 21 Aug 2017 16:09:21 +0200 Subject: [PATCH 03/83] PRNG and rendering the environment --- ex.py | 20 +++++++++++------- gym_maze/__init__.py | 14 ++++++++++--- gym_maze/envs/AbstractMaze.py | 29 ++++++++++++++++++++++----- gym_maze/envs/{Maze1.py => MazeF1.py} | 2 +- gym_maze/envs/MazeF2.py | 15 ++++++++++++++ gym_maze/envs/__init__.py | 3 ++- 6 files changed, 66 insertions(+), 17 deletions(-) rename gym_maze/envs/{Maze1.py => MazeF1.py} (91%) create mode 100644 gym_maze/envs/MazeF2.py diff --git a/ex.py b/ex.py index aeaf61c3..e82c2c00 100644 --- a/ex.py +++ b/ex.py @@ -1,25 +1,31 @@ +from random import choice + import logging import gym import gym_maze logger = logging.getLogger() -logger.setLevel(logging.INFO) +logger.setLevel(logging.DEBUG) + +env = gym.make('MazeF2-v0') -env = gym.make('Maze1-v0') +possible_actions = list(range(8)) for i_episode in range(1): observation = env.reset() for t in range(100): - env.render() - print(observation) - action = env.action_space.sample() + logger.info("Time: [{}], observation: [{}]".format(t, observation)) + + action = choice(possible_actions) + + logger.info("\t\tExecuted action: [{}]".format(action)) observation, reward, done, info = env.step(action) if done: - print("Episode finished after {} timesteps".format(t + 1)) + logger.info("Episode finished after {} timesteps.".format(t + 1)) + logger.info("Last reward: {}".format(reward)) break - logger.info("Finished") diff --git a/gym_maze/__init__.py b/gym_maze/__init__.py index 4b7eaa75..1f5486f7 100644 --- a/gym_maze/__init__.py +++ b/gym_maze/__init__.py @@ -8,7 +8,15 @@ logger = logging.getLogger(__name__) register( - id='Maze1-v0', - entry_point='gym_maze.envs:Maze1', - nondeterministic=True + id='MazeF1-v0', + entry_point='gym_maze.envs:MazeF1', + max_episode_steps=50, + nondeterministic=False +) + +register( + id='MazeF2-v0', + entry_point='gym_maze.envs:MazeF2', + max_episode_steps=50, + nondeterministic=False ) diff --git a/gym_maze/envs/AbstractMaze.py b/gym_maze/envs/AbstractMaze.py index 9eab4f38..a0ac74ac 100644 --- a/gym_maze/envs/AbstractMaze.py +++ b/gym_maze/envs/AbstractMaze.py @@ -7,10 +7,12 @@ import numpy as np import logging import random +import sys logger = logging.getLogger(__name__) +ANIMAT_MARKER = 5 ACTION_LOOKUP = { 0: 'N', 1: 'NE', @@ -34,7 +36,6 @@ def __init__(self, matrix): def _step(self, action): previous_observation = self._observe() - logger.debug("Previous observation: [{}]".format(previous_observation)) self._take_action(action, previous_observation) observation = self._observe() @@ -50,12 +51,17 @@ def _reset(self): def _render(self, mode='human', close=False): logging.debug("Rendering the environment") - ANIMAT_MARKER = '5' - situation = np.copy(self.maze.matrix) - situation[self.pos_y, self.pos_x] = ANIMAT_MARKER + if mode == 'human': + outfile = sys.stdout + outfile.write("\n") - logger.info("\n{}".format(situation)) + situation = np.copy(self.maze.matrix) + situation[self.pos_y, self.pos_x] = ANIMAT_MARKER + + for row in situation: + outfile.write(" ".join(self._render_element(el) for el in row)) + outfile.write("\n") def _observe(self): return self.maze.perception(self.pos_x, self.pos_y) @@ -122,3 +128,16 @@ def _insert_animat(self): @staticmethod def is_wall(perception): return perception == str(WALL_MAPPING) + + @staticmethod + def _render_element(el): + if el == 1: + return utils.colorize('■', 'gray') + elif el == 0: + return utils.colorize('□', 'white') + elif el == 9: + return utils.colorize('$', 'yellow') + elif el == ANIMAT_MARKER: + return utils.colorize('🚹', 'red') + else: + return utils.colorize(el, 'cyan') diff --git a/gym_maze/envs/Maze1.py b/gym_maze/envs/MazeF1.py similarity index 91% rename from gym_maze/envs/Maze1.py rename to gym_maze/envs/MazeF1.py index bb89803d..7bd3df10 100644 --- a/gym_maze/envs/Maze1.py +++ b/gym_maze/envs/MazeF1.py @@ -3,7 +3,7 @@ import numpy as np -class Maze1(AbstractMaze): +class MazeF1(AbstractMaze): def __init__(self): super().__init__(np.matrix([ [1, 1, 1, 1], diff --git a/gym_maze/envs/MazeF2.py b/gym_maze/envs/MazeF2.py new file mode 100644 index 00000000..1895dbcd --- /dev/null +++ b/gym_maze/envs/MazeF2.py @@ -0,0 +1,15 @@ +from gym_maze.envs import AbstractMaze + +import numpy as np + + +class MazeF2(AbstractMaze): + def __init__(self): + super().__init__(np.matrix([ + [1, 1, 1, 1, 1], + [1, 0, 0, 9, 1], + [1, 0, 1, 1, 1], + [1, 0, 0, 1, 1], + [1, 0, 1, 1, 1], + [1, 1, 1, 1, 1], + ])) diff --git a/gym_maze/envs/__init__.py b/gym_maze/envs/__init__.py index 5af22c1e..f475a04a 100644 --- a/gym_maze/envs/__init__.py +++ b/gym_maze/envs/__init__.py @@ -1,2 +1,3 @@ from gym_maze.envs.AbstractMaze import AbstractMaze -from gym_maze.envs.Maze1 import Maze1 +from gym_maze.envs.MazeF1 import MazeF1 +from gym_maze.envs.MazeF2 import MazeF2 From 57dcb08755315766615268bbebb9057ea58735e8 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 28 Aug 2017 21:52:14 +0200 Subject: [PATCH 04/83] Setup.py file updated --- .gitignore | 3 ++- setup.py | 16 +++++++++++++--- 2 files changed, 15 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index 64fe1bda..5f8026c0 100644 --- a/.gitignore +++ b/.gitignore @@ -99,4 +99,5 @@ ENV/ # mypy .mypy_cache/ -.idea \ No newline at end of file +.idea +.DS_Store \ No newline at end of file diff --git a/setup.py b/setup.py index d00178e9..b2948f4c 100644 --- a/setup.py +++ b/setup.py @@ -1,6 +1,16 @@ -from setuptools import setup +from setuptools import setup, find_packages setup(name='gym_maze', version='0.0.1', - install_requires=['gym'] -) + description='Maze environments for OpenAI Gym Environment', + keywords='acs lcs machine-learning reinforcement-learning opernai', + url='https://github.com/ParrotPrediction/openai-maze-envs', + author='Parrot Prediction', + author_email='contact@parrotprediction.com', + license='MIT', + packages=find_packages(), + install_requires=[ + 'gym' + ], + include_package_data=False, # We don't have other types of files + zip_safe=False) From a3a0f2bab361883bf2574aa6963ed2fef8276a5c Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 28 Aug 2017 21:57:16 +0200 Subject: [PATCH 05/83] Initial position --- gym_maze/envs/AbstractMaze.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/gym_maze/envs/AbstractMaze.py b/gym_maze/envs/AbstractMaze.py index a0ac74ac..9bb1f068 100644 --- a/gym_maze/envs/AbstractMaze.py +++ b/gym_maze/envs/AbstractMaze.py @@ -30,6 +30,8 @@ class AbstractMaze(gym.Env): def __init__(self, matrix): self.maze = Maze(matrix) + self.pos_x = None + self.pos_y = None self.action_space = spaces.Discrete(8) self.observation_space = spaces.Discrete(8) From 590afd4f92a50620a191eecd394c13b6b9493150 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 28 Aug 2017 22:00:11 +0200 Subject: [PATCH 06/83] Animat icon changed --- gym_maze/envs/AbstractMaze.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gym_maze/envs/AbstractMaze.py b/gym_maze/envs/AbstractMaze.py index 9bb1f068..8f29592a 100644 --- a/gym_maze/envs/AbstractMaze.py +++ b/gym_maze/envs/AbstractMaze.py @@ -140,6 +140,6 @@ def _render_element(el): elif el == 9: return utils.colorize('$', 'yellow') elif el == ANIMAT_MARKER: - return utils.colorize('🚹', 'red') + return utils.colorize('A', 'red') else: return utils.colorize(el, 'cyan') From 33a183d781d9de7fad2f84842996d4f8dfe6ad08 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Thu, 7 Sep 2017 06:41:45 +0200 Subject: [PATCH 07/83] New environments added --- ex.py | 2 +- gym_maze/__init__.py | 28 ++++++++++++++++++++++++++++ gym_maze/envs/BMaze4.py | 17 +++++++++++++++++ gym_maze/envs/Maze5.py | 18 ++++++++++++++++++ gym_maze/envs/MazeF3.py | 15 +++++++++++++++ gym_maze/envs/MazeF4.py | 15 +++++++++++++++ gym_maze/envs/__init__.py | 4 ++++ 7 files changed, 98 insertions(+), 1 deletion(-) create mode 100644 gym_maze/envs/BMaze4.py create mode 100644 gym_maze/envs/Maze5.py create mode 100644 gym_maze/envs/MazeF3.py create mode 100644 gym_maze/envs/MazeF4.py diff --git a/ex.py b/ex.py index e82c2c00..754fcb30 100644 --- a/ex.py +++ b/ex.py @@ -8,7 +8,7 @@ logger = logging.getLogger() logger.setLevel(logging.DEBUG) -env = gym.make('MazeF2-v0') +env = gym.make('BMaze4-v0') possible_actions = list(range(8)) diff --git a/gym_maze/__init__.py b/gym_maze/__init__.py index 1f5486f7..757b4c97 100644 --- a/gym_maze/__init__.py +++ b/gym_maze/__init__.py @@ -20,3 +20,31 @@ max_episode_steps=50, nondeterministic=False ) + +register( + id='MazeF3-v0', + entry_point='gym_maze.envs:MazeF3', + max_episode_steps=50, + nondeterministic=False +) + +register( + id='MazeF4-v0', + entry_point='gym_maze.envs:MazeF4', + max_episode_steps=50, + nondeterministic=True +) + +register( + id='Maze5-v0', + entry_point='gym_maze.envs:Maze5', + max_episode_steps=50, + nondeterministic=True +) + +register( + id='BMaze4-v0', + entry_point='gym_maze.envs:BMaze4', + max_episode_steps=50, + nondeterministic=False +) \ No newline at end of file diff --git a/gym_maze/envs/BMaze4.py b/gym_maze/envs/BMaze4.py new file mode 100644 index 00000000..1c37b1d7 --- /dev/null +++ b/gym_maze/envs/BMaze4.py @@ -0,0 +1,17 @@ +from gym_maze.envs import AbstractMaze + +import numpy as np + + +class BMaze4(AbstractMaze): + def __init__(self): + super().__init__(np.matrix([ + [1, 1, 1, 1, 1, 1, 1, 1], + [1, 0, 0, 1, 0, 0, 9, 1], + [1, 1, 0, 0, 1, 0, 0, 1], + [1, 1, 0, 1, 0, 0, 1, 1], + [1, 0, 0, 0, 0, 0, 0, 1], + [1, 1, 0, 1, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 1, 0, 1], + [1, 1, 1, 1, 1, 1, 1, 1], + ])) diff --git a/gym_maze/envs/Maze5.py b/gym_maze/envs/Maze5.py new file mode 100644 index 00000000..5ed538fa --- /dev/null +++ b/gym_maze/envs/Maze5.py @@ -0,0 +1,18 @@ +from gym_maze.envs import AbstractMaze + +import numpy as np + + +class Maze5(AbstractMaze): + def __init__(self): + super().__init__(np.matrix([ + [1, 1, 1, 1, 1, 1, 1, 1, 1], + [1, 0, 0, 0, 0, 0, 0, 9, 1], + [1, 0, 0, 1, 0, 1, 1, 0, 1], + [1, 0, 1, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 1, 1, 0, 0, 1], + [1, 0, 1, 0, 1, 0, 0, 1, 1], + [1, 0, 1, 0, 0, 1, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 1, 0, 1], + [1, 1, 1, 1, 1, 1, 1, 1, 1], + ])) diff --git a/gym_maze/envs/MazeF3.py b/gym_maze/envs/MazeF3.py new file mode 100644 index 00000000..380a0ccd --- /dev/null +++ b/gym_maze/envs/MazeF3.py @@ -0,0 +1,15 @@ +from gym_maze.envs import AbstractMaze + +import numpy as np + + +class MazeF3(AbstractMaze): + def __init__(self): + super().__init__(np.matrix([ + [1, 1, 1, 1, 1, 1], + [1, 0, 0, 0, 9, 1], + [1, 0, 1, 1, 1, 1], + [1, 0, 0, 0, 1, 1], + [1, 0, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1], + ])) \ No newline at end of file diff --git a/gym_maze/envs/MazeF4.py b/gym_maze/envs/MazeF4.py new file mode 100644 index 00000000..1267700e --- /dev/null +++ b/gym_maze/envs/MazeF4.py @@ -0,0 +1,15 @@ +from gym_maze.envs import AbstractMaze + +import numpy as np + + +class MazeF4(AbstractMaze): + def __init__(self): + super().__init__(np.matrix([ + [1, 1, 1, 1, 1, 1, 1], + [1, 0, 0, 0, 0, 9, 1], + [1, 0, 1, 1, 1, 1, 1], + [1, 0, 0, 0, 0, 1, 1], + [1, 0, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1, 1], + ])) diff --git a/gym_maze/envs/__init__.py b/gym_maze/envs/__init__.py index f475a04a..06fca194 100644 --- a/gym_maze/envs/__init__.py +++ b/gym_maze/envs/__init__.py @@ -1,3 +1,7 @@ from gym_maze.envs.AbstractMaze import AbstractMaze from gym_maze.envs.MazeF1 import MazeF1 from gym_maze.envs.MazeF2 import MazeF2 +from gym_maze.envs.MazeF3 import MazeF3 +from gym_maze.envs.MazeF4 import MazeF4 +from gym_maze.envs.Maze5 import Maze5 +from gym_maze.envs.BMaze4 import BMaze4 From 22e0570dd3bde7d92ee783a0edc8486a19e60a87 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Thu, 7 Sep 2017 15:22:14 +0200 Subject: [PATCH 08/83] Do not render the environment all the time --- gym_maze/envs/AbstractMaze.py | 6 +++++- requirements.txt | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/gym_maze/envs/AbstractMaze.py b/gym_maze/envs/AbstractMaze.py index 8f29592a..544bdc88 100644 --- a/gym_maze/envs/AbstractMaze.py +++ b/gym_maze/envs/AbstractMaze.py @@ -52,8 +52,10 @@ def _reset(self): return self._observe() def _render(self, mode='human', close=False): - logging.debug("Rendering the environment") + if close: + return + logging.debug("Rendering the environment") if mode == 'human': outfile = sys.stdout outfile.write("\n") @@ -64,6 +66,8 @@ def _render(self, mode='human', close=False): for row in situation: outfile.write(" ".join(self._render_element(el) for el in row)) outfile.write("\n") + else: + super(AbstractMaze, self).render(mode=mode) def _observe(self): return self.maze.perception(self.pos_x, self.pos_y) diff --git a/requirements.txt b/requirements.txt index b7dba22a..98682164 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1 @@ -gym==0.9.2 +gym==0.9.3 From 7e4e170221100ae5b51bbd892eec394a09493044 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Thu, 7 Sep 2017 15:35:14 +0200 Subject: [PATCH 09/83] Added Travis file --- .travis.yml | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 .travis.yml diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 00000000..2dbfb2c9 --- /dev/null +++ b/.travis.yml @@ -0,0 +1,13 @@ +language: python + +python: + - '3.5' + +deploy: + provider: pypi + user: khozzy + password: + secure: 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 + on: + tags: true + branch: master From 506fb4505bb3338379864c97f142933cb0395ac9 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Thu, 7 Sep 2017 15:39:53 +0200 Subject: [PATCH 10/83] Dummy Travis instruction --- .travis.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.travis.yml b/.travis.yml index 2dbfb2c9..9acf4e52 100644 --- a/.travis.yml +++ b/.travis.yml @@ -3,6 +3,9 @@ language: python python: - '3.5' +script: + - touch foo + deploy: provider: pypi user: khozzy From a9d6dbec402fa5c74880ad60645896707676e16f Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Fri, 8 Sep 2017 06:06:20 +0200 Subject: [PATCH 11/83] Typo corrected --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index b2948f4c..880cc4eb 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ setup(name='gym_maze', version='0.0.1', description='Maze environments for OpenAI Gym Environment', - keywords='acs lcs machine-learning reinforcement-learning opernai', + keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-maze-envs', author='Parrot Prediction', author_email='contact@parrotprediction.com', From f3d3eb0f30dedd001ef3e19ed466d517e18ee732 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Fri, 8 Sep 2017 07:40:49 +0200 Subject: [PATCH 12/83] Getting all possible transitions --- README.md | 10 ++++- ex.py | 7 ++-- gym_maze/Maze.py | 71 +++++++++++++++++++++++++++++++++++ gym_maze/__init__.py | 17 +++++++++ gym_maze/envs/AbstractMaze.py | 21 +++++------ gym_maze/utils/__init__.py | 1 + gym_maze/utils/utils.py | 58 ++++++++++++++++++++++++++++ requirements.txt | 1 + setup.py | 5 ++- 9 files changed, 174 insertions(+), 17 deletions(-) create mode 100644 gym_maze/utils/__init__.py create mode 100644 gym_maze/utils/utils.py diff --git a/README.md b/README.md index 1c109d8d..2325f1bb 100644 --- a/README.md +++ b/README.md @@ -1 +1,9 @@ -# openai-maze-envs \ No newline at end of file +# openai-maze-envs + +Initializing + + maze = gym.make('MazeF1-v0') + +Getting all possible transitions + + transitions = maze.env.get_all_possible_transitions() \ No newline at end of file diff --git a/ex.py b/ex.py index 754fcb30..99033e20 100644 --- a/ex.py +++ b/ex.py @@ -8,12 +8,13 @@ logger = logging.getLogger() logger.setLevel(logging.DEBUG) -env = gym.make('BMaze4-v0') +maze = gym.make('MazeF1-v0') possible_actions = list(range(8)) +transitions = maze.env.get_all_possible_transitions() for i_episode in range(1): - observation = env.reset() + observation = maze.reset() for t in range(100): logger.info("Time: [{}], observation: [{}]".format(t, observation)) @@ -21,7 +22,7 @@ action = choice(possible_actions) logger.info("\t\tExecuted action: [{}]".format(action)) - observation, reward, done, info = env.step(action) + observation, reward, done, info = maze.step(action) if done: logger.info("Episode finished after {} timesteps.".format(t + 1)) diff --git a/gym_maze/Maze.py b/gym_maze/Maze.py index a0325399..1f4ee114 100644 --- a/gym_maze/Maze.py +++ b/gym_maze/Maze.py @@ -107,3 +107,74 @@ def _within_x_range(self, x): def _within_y_range(self, y): return 0 <= y < self.max_y + + @staticmethod + def moved_north(start, destination) -> bool: + """ + :param start: start (X, Y) coordinates tuple + :param destination: destination (X, Y) coordinates tuple + :return: true if it was north move + """ + return destination[1] + 1 == start[1] + + @staticmethod + def moved_east(start, destination) -> bool: + """ + :param start: start (X, Y) coordinates tuple + :param destination: destination (X, Y) coordinates tuple + :return: true if it was east move + """ + return destination[0] - 1 == start[0] + + @staticmethod + def moved_south(start, destination) -> bool: + """ + :param start: start (X, Y) coordinates tuple + :param destination: destination (X, Y) coordinates tuple + :return: true if it was south move + """ + return destination[1] - 1 == start[1] + + @staticmethod + def moved_west(start, destination) -> bool: + """ + :param start: start (X, Y) coordinates tuple + :param destination: destination (X, Y) coordinates tuple + :return: true if it was west move + """ + return destination[0] + 1 == start[0] + + @staticmethod + def distinguish_direction(start, end): + direction = '' + + if Maze.moved_north(start, end): + direction += 'N' + + if Maze.moved_south(start, end): + direction += 'S' + + if Maze.moved_west(start, end): + direction += 'W' + + if Maze.moved_east(start, end): + direction += 'E' + + return direction + + @staticmethod + def get_possible_neighbour_cords(pos_x, pos_y) -> tuple: + """ + Returns a tuple with coordinates for + N, NE, E, SE, S, SW, W, NW neighbouring cells. + """ + n = (pos_x, pos_y - 1) + ne = (pos_x + 1, pos_y - 1) + e = (pos_x + 1, pos_y) + se = (pos_x + 1, pos_y + 1) + s = (pos_x, pos_y + 1) + sw = (pos_x - 1, pos_y + 1) + w = (pos_x - 1, pos_y) + nw = (pos_x - 1, pos_y - 1) + + return n, ne, e, se, s, sw, w, nw diff --git a/gym_maze/__init__.py b/gym_maze/__init__.py index 757b4c97..60c07168 100644 --- a/gym_maze/__init__.py +++ b/gym_maze/__init__.py @@ -7,6 +7,23 @@ logger = logging.getLogger(__name__) +ACTION_LOOKUP = { + 0: 'N', + 1: 'NE', + 2: 'E', + 3: 'SE', + 4: 'S', + 5: 'SW', + 6: 'W', + 7: 'NW' +} + + +def find_action_by_direction(direction): + for key, val in ACTION_LOOKUP.items(): + if val == direction: + return key + register( id='MazeF1-v0', entry_point='gym_maze.envs:MazeF1', diff --git a/gym_maze/envs/AbstractMaze.py b/gym_maze/envs/AbstractMaze.py index 544bdc88..1820590c 100644 --- a/gym_maze/envs/AbstractMaze.py +++ b/gym_maze/envs/AbstractMaze.py @@ -2,7 +2,8 @@ from gym import error, spaces, utils from gym.utils import seeding -from gym_maze import Maze, WALL_MAPPING +from gym_maze import Maze, WALL_MAPPING, ACTION_LOOKUP +from gym_maze.utils import get_all_possible_transitions import numpy as np import logging @@ -13,16 +14,6 @@ logger = logging.getLogger(__name__) ANIMAT_MARKER = 5 -ACTION_LOOKUP = { - 0: 'N', - 1: 'NE', - 2: 'E', - 3: 'SE', - 4: 'S', - 5: 'SW', - 6: 'W', - 7: 'NW' -} class AbstractMaze(gym.Env): @@ -81,6 +72,14 @@ def _get_reward(self): def _is_over(self): return self.maze.is_reward(self.pos_x, self.pos_y) + def get_all_possible_transitions(self): + """ + Debugging only + + :return: + """ + return get_all_possible_transitions(self) + def _take_action(self, action, observation): """Executes the action inside the maze""" animat_moved = False diff --git a/gym_maze/utils/__init__.py b/gym_maze/utils/__init__.py new file mode 100644 index 00000000..5cf88c30 --- /dev/null +++ b/gym_maze/utils/__init__.py @@ -0,0 +1 @@ +from .utils import get_all_possible_transitions diff --git a/gym_maze/utils/utils.py b/gym_maze/utils/utils.py new file mode 100644 index 00000000..aa2b65fc --- /dev/null +++ b/gym_maze/utils/utils.py @@ -0,0 +1,58 @@ +import networkx as nx + +from gym_maze import find_action_by_direction +from gym_maze.Maze import Maze + + +def get_all_possible_transitions(maze): + """ + Returns all possible transitions within the maze. + [POINT]->[ACTION]->[POINT] + This information is used to calculate the agent's knowledge + :param maze: an instance of the maze + :return: + """ + transitions = [] + + g = _create_graph(maze) + + path_nodes = (node for node, data + in g.nodes_iter(data=True) if data['type'] == 'path') + + for node in path_nodes: + for neighbour in nx.all_neighbors(g, node): + direction = Maze.distinguish_direction(node, neighbour) + action = find_action_by_direction(direction) + + transitions.append((node, action, neighbour)) + + return transitions + + +def _create_graph(env): + maze = env.maze + + # Create uni-directed graph + g = nx.Graph() + + # Add nodes + for x in range(0, maze.max_x): + for y in range(0, maze.max_y): + if maze.is_path(x, y): + g.add_node((x, y), type='path') + if maze.is_reward(x, y): + g.add_node((x, y), type='reward') + + # Add edges + path_nodes = [cords for cords, attribs + in g.nodes(data=True) if attribs['type'] == 'path'] + + for n in path_nodes: + neighbour_cells = Maze.get_possible_neighbour_cords(*n) + allowed_cells = [c for c in neighbour_cells + if maze.is_path(*c) or maze.is_reward(*c)] + edges = [(n, dest) for dest in allowed_cells] + + g.add_edges_from(edges) + + return g \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 98682164..63c46c78 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1 +1,2 @@ gym==0.9.3 +networkx==1.11 \ No newline at end of file diff --git a/setup.py b/setup.py index 880cc4eb..421dc3dc 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='gym_maze', - version='0.0.1', + version='0.0.2', description='Maze environments for OpenAI Gym Environment', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-maze-envs', @@ -10,7 +10,8 @@ license='MIT', packages=find_packages(), install_requires=[ - 'gym' + 'gym', + 'networkx' ], include_package_data=False, # We don't have other types of files zip_safe=False) From cd25554c5d92c693490257ecf2dff0bb27941f2b Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Sat, 4 Nov 2017 16:35:42 +0100 Subject: [PATCH 13/83] Gym version updated --- requirements.txt | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 63c46c78..4bfbcf29 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,2 @@ -gym==0.9.3 +gym==0.9.4 networkx==1.11 \ No newline at end of file diff --git a/setup.py b/setup.py index 421dc3dc..c0ebe360 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='gym_maze', - version='0.0.2', + version='0.0.3', description='Maze environments for OpenAI Gym Environment', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-maze-envs', From 046fd5c6d5b528537529be7a37ea1410c0cfc953 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Tue, 7 Nov 2017 13:01:05 +0100 Subject: [PATCH 14/83] Add Woods1 environment --- gym_maze/__init__.py | 7 +++++++ gym_maze/envs/Woods1.py | 14 ++++++++++++++ gym_maze/envs/__init__.py | 1 + setup.py | 2 +- 4 files changed, 23 insertions(+), 1 deletion(-) create mode 100644 gym_maze/envs/Woods1.py diff --git a/gym_maze/__init__.py b/gym_maze/__init__.py index 60c07168..27e0e33f 100644 --- a/gym_maze/__init__.py +++ b/gym_maze/__init__.py @@ -64,4 +64,11 @@ def find_action_by_direction(direction): entry_point='gym_maze.envs:BMaze4', max_episode_steps=50, nondeterministic=False +) + +register( + id='Woods1-v0', + entry_point='gym_maze.envs:Woods1', + max_episode_steps=50, + nondeterministic=False ) \ No newline at end of file diff --git a/gym_maze/envs/Woods1.py b/gym_maze/envs/Woods1.py new file mode 100644 index 00000000..edc0b1e2 --- /dev/null +++ b/gym_maze/envs/Woods1.py @@ -0,0 +1,14 @@ +from gym_maze.envs import AbstractMaze + +import numpy as np + + +class Woods1(AbstractMaze): + def __init__(self): + super().__init__(np.matrix([ + [1, 1, 1, 1, 1], + [1, 0, 0, 9, 1], + [1, 0, 0, 0, 1], + [1, 0, 0, 0, 1], + [1, 1, 1, 1, 1], + ])) diff --git a/gym_maze/envs/__init__.py b/gym_maze/envs/__init__.py index 06fca194..f745ec03 100644 --- a/gym_maze/envs/__init__.py +++ b/gym_maze/envs/__init__.py @@ -5,3 +5,4 @@ from gym_maze.envs.MazeF4 import MazeF4 from gym_maze.envs.Maze5 import Maze5 from gym_maze.envs.BMaze4 import BMaze4 +from gym_maze.envs.Woods1 import Woods1 diff --git a/setup.py b/setup.py index c0ebe360..5e6f696c 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='gym_maze', - version='0.0.3', + version='0.0.4', description='Maze environments for OpenAI Gym Environment', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-maze-envs', From 719ec07ba41af59bcb5ae7d149d225e51900ac40 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Tue, 7 Nov 2017 19:25:53 +0100 Subject: [PATCH 15/83] tests, import, versions fixed --- .travis.yml | 2 ++ gym_maze/tests/__init__.py | 0 gym_maze/tests/test_utils.py | 14 ++++++++++++++ gym_maze/utils/utils.py | 2 +- requirements.txt | 3 ++- setup.py | 6 +++--- 6 files changed, 22 insertions(+), 5 deletions(-) create mode 100644 gym_maze/tests/__init__.py create mode 100644 gym_maze/tests/test_utils.py diff --git a/.travis.yml b/.travis.yml index 9acf4e52..91e25df4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -5,6 +5,8 @@ python: script: - touch foo + - pip3 install -r requirements.txt + - pytest deploy: provider: pypi diff --git a/gym_maze/tests/__init__.py b/gym_maze/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/gym_maze/tests/test_utils.py b/gym_maze/tests/test_utils.py new file mode 100644 index 00000000..d491083c --- /dev/null +++ b/gym_maze/tests/test_utils.py @@ -0,0 +1,14 @@ +import gym +import gym_maze + + +class TestUtils: + def test_should_calculate_transitions(self): + # given + maze = gym.make("Woods1-v0") + + # when + transitions = maze.env.get_all_possible_transitions() + + # then + assert 37 == len(transitions) diff --git a/gym_maze/utils/utils.py b/gym_maze/utils/utils.py index aa2b65fc..a3f8abce 100644 --- a/gym_maze/utils/utils.py +++ b/gym_maze/utils/utils.py @@ -17,7 +17,7 @@ def get_all_possible_transitions(maze): g = _create_graph(maze) path_nodes = (node for node, data - in g.nodes_iter(data=True) if data['type'] == 'path') + in g.nodes(data=True) if data['type'] == 'path') for node in path_nodes: for neighbour in nx.all_neighbors(g, node): diff --git a/requirements.txt b/requirements.txt index 4bfbcf29..f43a1ad2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,2 +1,3 @@ gym==0.9.4 -networkx==1.11 \ No newline at end of file +networkx==2.0 +pytest==3.2.3 \ No newline at end of file diff --git a/setup.py b/setup.py index 5e6f696c..62a4e148 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='gym_maze', - version='0.0.4', + version='0.0.6', description='Maze environments for OpenAI Gym Environment', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-maze-envs', @@ -10,8 +10,8 @@ license='MIT', packages=find_packages(), install_requires=[ - 'gym', - 'networkx' + 'gym==0.9.4', + 'networkx==2.0' ], include_package_data=False, # We don't have other types of files zip_safe=False) From c61ec17576ba6bce18c6b35863af3626f4571d4e Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Sat, 18 Nov 2017 18:36:49 +0100 Subject: [PATCH 16/83] Boolean Multiplexer environment --- README.md | 9 +++- ex.py | 32 -------------- examples/maze.py | 33 ++++++++++++++ gym_maze/Maze.py | 4 -- gym_maze/__init__.py | 8 +--- gym_maze/envs/AbstractMaze.py | 16 +++---- gym_maze/tests/test_utils.py | 1 + gym_multiplexer/__init__.py | 27 ++++++++++++ gym_multiplexer/boolean_multiplexer.py | 52 +++++++++++++++++++++++ gym_multiplexer/tests/__init__.py | 0 gym_multiplexer/tests/test_multiplexer.py | 52 +++++++++++++++++++++++ requirements.txt | 4 +- setup.py | 9 ++-- 13 files changed, 187 insertions(+), 60 deletions(-) delete mode 100644 ex.py create mode 100644 examples/maze.py create mode 100644 gym_multiplexer/__init__.py create mode 100644 gym_multiplexer/boolean_multiplexer.py create mode 100644 gym_multiplexer/tests/__init__.py create mode 100644 gym_multiplexer/tests/test_multiplexer.py diff --git a/README.md b/README.md index 2325f1bb..04eaea13 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,6 @@ -# openai-maze-envs +# Parrot Prediction OpenAI environments + +## Maze Initializing @@ -6,4 +8,7 @@ Initializing Getting all possible transitions - transitions = maze.env.get_all_possible_transitions() \ No newline at end of file + transitions = maze.env.get_all_possible_transitions() + +## Boolean Multiplexer +Read blog [post](https://medium.com/parrot-prediction/boolean-multiplexer-in-practice-94e3236821b5) describing the usage. \ No newline at end of file diff --git a/ex.py b/ex.py deleted file mode 100644 index 99033e20..00000000 --- a/ex.py +++ /dev/null @@ -1,32 +0,0 @@ -from random import choice - -import logging - -import gym -import gym_maze - -logger = logging.getLogger() -logger.setLevel(logging.DEBUG) - -maze = gym.make('MazeF1-v0') - -possible_actions = list(range(8)) -transitions = maze.env.get_all_possible_transitions() - -for i_episode in range(1): - observation = maze.reset() - - for t in range(100): - logger.info("Time: [{}], observation: [{}]".format(t, observation)) - - action = choice(possible_actions) - - logger.info("\t\tExecuted action: [{}]".format(action)) - observation, reward, done, info = maze.step(action) - - if done: - logger.info("Episode finished after {} timesteps.".format(t + 1)) - logger.info("Last reward: {}".format(reward)) - break - -logger.info("Finished") diff --git a/examples/maze.py b/examples/maze.py new file mode 100644 index 00000000..0d9d24d0 --- /dev/null +++ b/examples/maze.py @@ -0,0 +1,33 @@ +import logging +from random import choice + +import gym + +# noinspection PyUnresolvedReferences +import gym_maze + +logging.basicConfig(level=logging.DEBUG) + +if __name__ == '__main__': + maze = gym.make('MazeF1-v0') + + possible_actions = list(range(8)) + transitions = maze.env.get_all_possible_transitions() + + for i_episode in range(1): + observation = maze.reset() + + for t in range(100): + logging.info("Time: [{}], observation: [{}]".format(t, observation)) + + action = choice(possible_actions) + + logging.info("\t\tExecuted action: [{}]".format(action)) + observation, reward, done, info = maze.step(action) + + if done: + logging.info("Episode finished after {} timesteps.".format(t + 1)) + logging.info("Last reward: {}".format(reward)) + break + + logging.info("Finished") diff --git a/gym_maze/Maze.py b/gym_maze/Maze.py index 1f4ee114..bf593479 100644 --- a/gym_maze/Maze.py +++ b/gym_maze/Maze.py @@ -1,7 +1,3 @@ -import logging - -logger = logging.getLogger(__name__) - PATH_MAPPING = 0 WALL_MAPPING = 1 REWARD_MAPPING = 9 diff --git a/gym_maze/__init__.py b/gym_maze/__init__.py index 27e0e33f..cc59457f 100644 --- a/gym_maze/__init__.py +++ b/gym_maze/__init__.py @@ -1,11 +1,7 @@ -import logging - from gym.envs.registration import register -from gym_maze.Maze import Maze -from gym_maze.Maze import PATH_MAPPING, WALL_MAPPING, REWARD_MAPPING - -logger = logging.getLogger(__name__) +# noinspection PyUnresolvedReferences +from gym_maze.Maze import Maze, PATH_MAPPING, WALL_MAPPING, REWARD_MAPPING ACTION_LOOKUP = { 0: 'N', diff --git a/gym_maze/envs/AbstractMaze.py b/gym_maze/envs/AbstractMaze.py index 1820590c..1303ac44 100644 --- a/gym_maze/envs/AbstractMaze.py +++ b/gym_maze/envs/AbstractMaze.py @@ -1,17 +1,13 @@ -import gym -from gym import error, spaces, utils -from gym.utils import seeding - -from gym_maze import Maze, WALL_MAPPING, ACTION_LOOKUP -from gym_maze.utils import get_all_possible_transitions - -import numpy as np import logging import random import sys +import gym +import numpy as np +from gym import spaces, utils -logger = logging.getLogger(__name__) +from gym_maze import Maze, WALL_MAPPING, ACTION_LOOKUP +from gym_maze.utils import get_all_possible_transitions ANIMAT_MARKER = 5 @@ -38,7 +34,7 @@ def _step(self, action): return observation, reward, episode_over, {} def _reset(self): - logger.debug("Resetting the environment") + logging.debug("Resetting the environment") self._insert_animat() return self._observe() diff --git a/gym_maze/tests/test_utils.py b/gym_maze/tests/test_utils.py index d491083c..7031072d 100644 --- a/gym_maze/tests/test_utils.py +++ b/gym_maze/tests/test_utils.py @@ -1,4 +1,5 @@ import gym +# noinspection PyUnresolvedReferences import gym_maze diff --git a/gym_multiplexer/__init__.py b/gym_multiplexer/__init__.py new file mode 100644 index 00000000..4a79dca0 --- /dev/null +++ b/gym_multiplexer/__init__.py @@ -0,0 +1,27 @@ +from .boolean_multiplexer import BooleanMultiplexer + +from gym.envs.registration import register + +name = "boolean-multiplexer" +max_episode_steps = 1 + +register( + id='{}-3bit-v0'.format(name), + entry_point='gym_multiplexer:BooleanMultiplexer', + max_episode_steps=max_episode_steps, + kwargs={'control_bits': 1} +) + +register( + id='{}-6bit-v0'.format(name), + entry_point='gym_multiplexer:BooleanMultiplexer', + max_episode_steps=max_episode_steps, + kwargs={'control_bits': 2} +) + +register( + id='{}-11bit-v0'.format(name), + entry_point='gym_multiplexer:BooleanMultiplexer', + max_episode_steps=max_episode_steps, + kwargs={'control_bits': 3} +) \ No newline at end of file diff --git a/gym_multiplexer/boolean_multiplexer.py b/gym_multiplexer/boolean_multiplexer.py new file mode 100644 index 00000000..66179519 --- /dev/null +++ b/gym_multiplexer/boolean_multiplexer.py @@ -0,0 +1,52 @@ +import logging +import random + +import gym +from bitstring import BitArray +from gym.spaces import Discrete + + +class BooleanMultiplexer(gym.Env): + + def __init__(self, control_bits=3) -> None: + self.metadata = {'render.modes': ['human']} + self.control_bits = control_bits + self.observation_space = Discrete(len(self._observation_string_length)) + self.action_space = Discrete(2) + + def _reset(self): + logging.debug("Resetting the environment") + bits = BitArray([random.randint(0, 1) for _ in + self._observation_string_length]) + + self._ctrl_bits = bits[:self.control_bits] + self._data_bits = bits[self.control_bits:] + + def _step(self, action): + state = self._observation() + reward = 0 + + if action == self._answer: + reward = 1 + + return state, reward, None, None + + def _render(self, mode='human', close=False): + if close: + return + + if mode == 'human': + return self.control_bits + self._data_bits + else: + super(BooleanMultiplexer, self).render(mode=mode) + + def _observation(self): + return self.control_bits + self._data_bits + + @property + def _observation_string_length(self): + return range(0, self.control_bits + pow(2, self.control_bits)) + + @property + def _answer(self): + return int(self._data_bits[self._ctrl_bits.uint]) diff --git a/gym_multiplexer/tests/__init__.py b/gym_multiplexer/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/gym_multiplexer/tests/test_multiplexer.py b/gym_multiplexer/tests/test_multiplexer.py new file mode 100644 index 00000000..2d89e0c0 --- /dev/null +++ b/gym_multiplexer/tests/test_multiplexer.py @@ -0,0 +1,52 @@ +import logging +import random +import sys + +import gym + +# noinspection PyUnresolvedReferences +import gym_multiplexer + +logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) + + +class TestMultiplexer: + + def test_should_initialize_multiplexer(self): + # when + mp = gym.make('boolean-multiplexer-6bit-v0') + + # then + assert mp is not None + assert 6 == mp.observation_space.n + assert 2 == mp.action_space.n + + def test_should_render_state(self): + # given + mp = gym.make('boolean-multiplexer-3bit-v0') + mp.reset() + + # when + state = mp.render() + + # then + assert state is not None + assert 3 == len(state) + + def test_should_execute_step(self): + # given + mp = gym.make('boolean-multiplexer-3bit-v0') + mp.reset() + action = self._random_action() + + # when + state, reward, done, _ = mp.step(action) + + # then + assert state is not None + assert reward in [0, 1] + assert done is True + + @staticmethod + def _random_action(): + return random.sample([0, 1], 1) diff --git a/requirements.txt b/requirements.txt index f43a1ad2..be0797d7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ -gym==0.9.4 -networkx==2.0 +-e . + pytest==3.2.3 \ No newline at end of file diff --git a/setup.py b/setup.py index 62a4e148..118db805 100644 --- a/setup.py +++ b/setup.py @@ -1,8 +1,8 @@ from setuptools import setup, find_packages -setup(name='gym_maze', - version='0.0.6', - description='Maze environments for OpenAI Gym Environment', +setup(name='parrot_envs', + version='0.0.7', + description='Custom environments for OpenAI Gym Environment', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-maze-envs', author='Parrot Prediction', @@ -11,7 +11,8 @@ packages=find_packages(), install_requires=[ 'gym==0.9.4', - 'networkx==2.0' + 'networkx==2.0', + 'bitstring==3.1.5' ], include_package_data=False, # We don't have other types of files zip_safe=False) From b599b888092b0aaa70b8132c562990a3c736e1ce Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Sat, 18 Nov 2017 21:53:56 +0100 Subject: [PATCH 17/83] Better package naming --- gym_maze/__init__.py | 2 +- gym_maze/envs/__init__.py | 2 +- .../envs/{AbstractMaze.py => abstract_maze.py} | 3 ++- gym_maze/{Maze.py => maze.py} | 0 gym_maze/utils/utils.py | 2 +- gym_multiplexer/boolean_multiplexer.py | 9 +++++---- gym_multiplexer/tests/test_multiplexer.py | 2 ++ gym_multiplexer/tests/test_utils.py | 14 ++++++++++++++ gym_multiplexer/utils/__init__.py | 1 + gym_multiplexer/utils/utils.py | 10 ++++++++++ setup.py | 4 ++-- 11 files changed, 39 insertions(+), 10 deletions(-) rename gym_maze/envs/{AbstractMaze.py => abstract_maze.py} (98%) rename gym_maze/{Maze.py => maze.py} (100%) create mode 100644 gym_multiplexer/tests/test_utils.py create mode 100644 gym_multiplexer/utils/__init__.py create mode 100644 gym_multiplexer/utils/utils.py diff --git a/gym_maze/__init__.py b/gym_maze/__init__.py index cc59457f..74838c0c 100644 --- a/gym_maze/__init__.py +++ b/gym_maze/__init__.py @@ -1,7 +1,7 @@ from gym.envs.registration import register # noinspection PyUnresolvedReferences -from gym_maze.Maze import Maze, PATH_MAPPING, WALL_MAPPING, REWARD_MAPPING +from .maze import Maze, PATH_MAPPING, WALL_MAPPING, REWARD_MAPPING ACTION_LOOKUP = { 0: 'N', diff --git a/gym_maze/envs/__init__.py b/gym_maze/envs/__init__.py index f745ec03..9f1adf5f 100644 --- a/gym_maze/envs/__init__.py +++ b/gym_maze/envs/__init__.py @@ -1,4 +1,4 @@ -from gym_maze.envs.AbstractMaze import AbstractMaze +from gym_maze.envs.abstract_maze import AbstractMaze from gym_maze.envs.MazeF1 import MazeF1 from gym_maze.envs.MazeF2 import MazeF2 from gym_maze.envs.MazeF3 import MazeF3 diff --git a/gym_maze/envs/AbstractMaze.py b/gym_maze/envs/abstract_maze.py similarity index 98% rename from gym_maze/envs/AbstractMaze.py rename to gym_maze/envs/abstract_maze.py index 1303ac44..668994f0 100644 --- a/gym_maze/envs/AbstractMaze.py +++ b/gym_maze/envs/abstract_maze.py @@ -6,7 +6,8 @@ import numpy as np from gym import spaces, utils -from gym_maze import Maze, WALL_MAPPING, ACTION_LOOKUP +from gym_maze import ACTION_LOOKUP +from gym_maze.maze import Maze, WALL_MAPPING from gym_maze.utils import get_all_possible_transitions ANIMAT_MARKER = 5 diff --git a/gym_maze/Maze.py b/gym_maze/maze.py similarity index 100% rename from gym_maze/Maze.py rename to gym_maze/maze.py diff --git a/gym_maze/utils/utils.py b/gym_maze/utils/utils.py index a3f8abce..28c758af 100644 --- a/gym_maze/utils/utils.py +++ b/gym_maze/utils/utils.py @@ -1,7 +1,7 @@ import networkx as nx from gym_maze import find_action_by_direction -from gym_maze.Maze import Maze +from gym_maze.maze import Maze def get_all_possible_transitions(maze): diff --git a/gym_multiplexer/boolean_multiplexer.py b/gym_multiplexer/boolean_multiplexer.py index 66179519..d6563a89 100644 --- a/gym_multiplexer/boolean_multiplexer.py +++ b/gym_multiplexer/boolean_multiplexer.py @@ -9,8 +9,8 @@ class BooleanMultiplexer(gym.Env): def __init__(self, control_bits=3) -> None: - self.metadata = {'render.modes': ['human']} self.control_bits = control_bits + self.metadata = {'render.modes': ['human']} self.observation_space = Discrete(len(self._observation_string_length)) self.action_space = Discrete(2) @@ -36,12 +36,13 @@ def _render(self, mode='human', close=False): return if mode == 'human': - return self.control_bits + self._data_bits + return self._observation() else: super(BooleanMultiplexer, self).render(mode=mode) - def _observation(self): - return self.control_bits + self._data_bits + def _observation(self) -> str: + bit_array = self._ctrl_bits + self._data_bits + return bit_array.bin @property def _observation_string_length(self): diff --git a/gym_multiplexer/tests/test_multiplexer.py b/gym_multiplexer/tests/test_multiplexer.py index 2d89e0c0..33dbc8a0 100644 --- a/gym_multiplexer/tests/test_multiplexer.py +++ b/gym_multiplexer/tests/test_multiplexer.py @@ -32,6 +32,7 @@ def test_should_render_state(self): # then assert state is not None assert 3 == len(state) + assert type(state) is str def test_should_execute_step(self): # given @@ -44,6 +45,7 @@ def test_should_execute_step(self): # then assert state is not None + assert type(state) is str assert reward in [0, 1] assert done is True diff --git a/gym_multiplexer/tests/test_utils.py b/gym_multiplexer/tests/test_utils.py new file mode 100644 index 00000000..6d1d0e74 --- /dev/null +++ b/gym_multiplexer/tests/test_utils.py @@ -0,0 +1,14 @@ +from gym_multiplexer.utils import get_correct_answer + + +class TestUtils: + + def test_should_calculate_correct_answer_for_3bit_multiplexer(self): + assert 1 == get_correct_answer('010', 1) + assert 0 == get_correct_answer('110', 1) + + def test_should_calculate_correct_answer_for_6bit_multiplexer(self): + assert 0 == get_correct_answer('110100', 2) + + def test_should_calculate_correct_answer_for_11bit_multiplexer(self): + assert 1 == get_correct_answer('101101101', 3) diff --git a/gym_multiplexer/utils/__init__.py b/gym_multiplexer/utils/__init__.py new file mode 100644 index 00000000..cd0c6656 --- /dev/null +++ b/gym_multiplexer/utils/__init__.py @@ -0,0 +1 @@ +from .utils import get_correct_answer \ No newline at end of file diff --git a/gym_multiplexer/utils/utils.py b/gym_multiplexer/utils/utils.py new file mode 100644 index 00000000..2c60652e --- /dev/null +++ b/gym_multiplexer/utils/utils.py @@ -0,0 +1,10 @@ +from bitstring import BitString, BitArray + + +def get_correct_answer(bitstring: str, control_bits: int) -> int: + bits = BitString(bin=bitstring) + + _ctrl_bits = bits[:control_bits] + _data_bits = bits[control_bits:] + + return int(_data_bits[_ctrl_bits.uint]) diff --git a/setup.py b/setup.py index 118db805..5dd65aca 100644 --- a/setup.py +++ b/setup.py @@ -1,8 +1,8 @@ from setuptools import setup, find_packages setup(name='parrot_envs', - version='0.0.7', - description='Custom environments for OpenAI Gym Environment', + version='0.8', + description='Custom environments for OpenAI Gym', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-maze-envs', author='Parrot Prediction', From 9c36c872df41371a036526ac77121b461bd0c853 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 20 Nov 2017 11:33:27 +0100 Subject: [PATCH 18/83] Validation bit for multiplexer --- gym_multiplexer/boolean_multiplexer.py | 18 +++++++++++------ gym_multiplexer/tests/test_multiplexer.py | 24 ++++++++++++++++++----- gym_multiplexer/tests/test_utils.py | 8 ++++---- gym_multiplexer/utils/utils.py | 1 + 4 files changed, 36 insertions(+), 15 deletions(-) diff --git a/gym_multiplexer/boolean_multiplexer.py b/gym_multiplexer/boolean_multiplexer.py index d6563a89..0ffae071 100644 --- a/gym_multiplexer/boolean_multiplexer.py +++ b/gym_multiplexer/boolean_multiplexer.py @@ -8,26 +8,33 @@ class BooleanMultiplexer(gym.Env): + REWARD = 1000 + def __init__(self, control_bits=3) -> None: self.control_bits = control_bits self.metadata = {'render.modes': ['human']} - self.observation_space = Discrete(len(self._observation_string_length)) + self.observation_space = Discrete(self._observation_string_length) self.action_space = Discrete(2) def _reset(self): logging.debug("Resetting the environment") bits = BitArray([random.randint(0, 1) for _ in - self._observation_string_length]) + range(0, self._observation_string_length)]) + bits[-1] = False # set validation bit to False self._ctrl_bits = bits[:self.control_bits] self._data_bits = bits[self.control_bits:] + self._validation_bit = bits[-1] + + return self._observation() def _step(self, action): state = self._observation() reward = 0 if action == self._answer: - reward = 1 + state = state[:-1] + '1' # set validation bit to True + reward = self.REWARD return state, reward, None, None @@ -41,12 +48,11 @@ def _render(self, mode='human', close=False): super(BooleanMultiplexer, self).render(mode=mode) def _observation(self) -> str: - bit_array = self._ctrl_bits + self._data_bits - return bit_array.bin + return (self._ctrl_bits + self._data_bits + self._validation_bit).bin @property def _observation_string_length(self): - return range(0, self.control_bits + pow(2, self.control_bits)) + return self.control_bits + pow(2, self.control_bits) + 1 @property def _answer(self): diff --git a/gym_multiplexer/tests/test_multiplexer.py b/gym_multiplexer/tests/test_multiplexer.py index 33dbc8a0..5f219c14 100644 --- a/gym_multiplexer/tests/test_multiplexer.py +++ b/gym_multiplexer/tests/test_multiplexer.py @@ -11,16 +11,28 @@ class TestMultiplexer: - def test_should_initialize_multiplexer(self): # when mp = gym.make('boolean-multiplexer-6bit-v0') # then assert mp is not None - assert 6 == mp.observation_space.n + assert 7 == mp.observation_space.n assert 2 == mp.action_space.n + def test_should_return_observation_when_reset(self): + # given + mp = gym.make('boolean-multiplexer-6bit-v0') + + # when + state = mp.reset() + + # then + assert state is not None + assert state[-1] == '0' + assert 7 == len(state) + assert type(state) is str + def test_should_render_state(self): # given mp = gym.make('boolean-multiplexer-3bit-v0') @@ -31,7 +43,8 @@ def test_should_render_state(self): # then assert state is not None - assert 3 == len(state) + assert state[-1] == '0' + assert 4 == len(state) assert type(state) is str def test_should_execute_step(self): @@ -45,10 +58,11 @@ def test_should_execute_step(self): # then assert state is not None + assert state[-1] in ['0', '1'] assert type(state) is str - assert reward in [0, 1] + assert reward in [0, 1000] assert done is True @staticmethod def _random_action(): - return random.sample([0, 1], 1) + return random.sample([0, 1], 1)[0] diff --git a/gym_multiplexer/tests/test_utils.py b/gym_multiplexer/tests/test_utils.py index 6d1d0e74..fe47d2c6 100644 --- a/gym_multiplexer/tests/test_utils.py +++ b/gym_multiplexer/tests/test_utils.py @@ -4,11 +4,11 @@ class TestUtils: def test_should_calculate_correct_answer_for_3bit_multiplexer(self): - assert 1 == get_correct_answer('010', 1) - assert 0 == get_correct_answer('110', 1) + assert 1 == get_correct_answer('0100', 1) + assert 0 == get_correct_answer('1100', 1) def test_should_calculate_correct_answer_for_6bit_multiplexer(self): - assert 0 == get_correct_answer('110100', 2) + assert 0 == get_correct_answer('1101000', 2) def test_should_calculate_correct_answer_for_11bit_multiplexer(self): - assert 1 == get_correct_answer('101101101', 3) + assert 1 == get_correct_answer('1011011010', 3) diff --git a/gym_multiplexer/utils/utils.py b/gym_multiplexer/utils/utils.py index 2c60652e..3ac54e8f 100644 --- a/gym_multiplexer/utils/utils.py +++ b/gym_multiplexer/utils/utils.py @@ -6,5 +6,6 @@ def get_correct_answer(bitstring: str, control_bits: int) -> int: _ctrl_bits = bits[:control_bits] _data_bits = bits[control_bits:] + _validation_bit = bits[-1] return int(_data_bits[_ctrl_bits.uint]) From 149334b85791b4fce912fc48394ebe31542e7f17 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 20 Nov 2017 20:42:47 +0100 Subject: [PATCH 19/83] Bump version --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 5dd65aca..b70bbbbb 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='parrot_envs', - version='0.8', + version='0.9', description='Custom environments for OpenAI Gym', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-maze-envs', From 1263547191f9a0f1816cdf2398a1701ba53eeb14 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Tue, 21 Nov 2017 19:32:47 +0100 Subject: [PATCH 20/83] Fix perception string issue --- gym_multiplexer/boolean_multiplexer.py | 13 ++++++------- gym_multiplexer/tests/test_multiplexer.py | 14 ++++++++++++++ gym_multiplexer/utils/utils.py | 2 +- setup.py | 2 +- 4 files changed, 22 insertions(+), 9 deletions(-) diff --git a/gym_multiplexer/boolean_multiplexer.py b/gym_multiplexer/boolean_multiplexer.py index 0ffae071..d9c0c6f7 100644 --- a/gym_multiplexer/boolean_multiplexer.py +++ b/gym_multiplexer/boolean_multiplexer.py @@ -19,24 +19,22 @@ def __init__(self, control_bits=3) -> None: def _reset(self): logging.debug("Resetting the environment") bits = BitArray([random.randint(0, 1) for _ in - range(0, self._observation_string_length)]) - bits[-1] = False # set validation bit to False + range(0, self._observation_string_length - 1)]) self._ctrl_bits = bits[:self.control_bits] self._data_bits = bits[self.control_bits:] - self._validation_bit = bits[-1] + self._validation_bit = False return self._observation() def _step(self, action): - state = self._observation() reward = 0 if action == self._answer: - state = state[:-1] + '1' # set validation bit to True + self._validation_bit = True reward = self.REWARD - return state, reward, None, None + return self._observation(), reward, None, None def _render(self, mode='human', close=False): if close: @@ -48,7 +46,8 @@ def _render(self, mode='human', close=False): super(BooleanMultiplexer, self).render(mode=mode) def _observation(self) -> str: - return (self._ctrl_bits + self._data_bits + self._validation_bit).bin + return (self._ctrl_bits + self._data_bits + + BitArray([self._validation_bit])).bin @property def _observation_string_length(self): diff --git a/gym_multiplexer/tests/test_multiplexer.py b/gym_multiplexer/tests/test_multiplexer.py index 5f219c14..689cc2ba 100644 --- a/gym_multiplexer/tests/test_multiplexer.py +++ b/gym_multiplexer/tests/test_multiplexer.py @@ -63,6 +63,20 @@ def test_should_execute_step(self): assert reward in [0, 1000] assert done is True + def test_execute_multiple_steps_and_keep_constant_perception_length(self): + # given + mp = gym.make('boolean-multiplexer-6bit-v0') + steps = 100 + + # when & then + for _ in range(0, steps): + p0 = mp.reset() + assert 7 == len(p0) + + action = self._random_action() + p1, reward, done, _ = mp.step(action) + assert 7 == len(p1) + @staticmethod def _random_action(): return random.sample([0, 1], 1)[0] diff --git a/gym_multiplexer/utils/utils.py b/gym_multiplexer/utils/utils.py index 3ac54e8f..ecbf15a4 100644 --- a/gym_multiplexer/utils/utils.py +++ b/gym_multiplexer/utils/utils.py @@ -1,4 +1,4 @@ -from bitstring import BitString, BitArray +from bitstring import BitString def get_correct_answer(bitstring: str, control_bits: int) -> int: diff --git a/setup.py b/setup.py index b70bbbbb..0a334dbe 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='parrot_envs', - version='0.9', + version='0.9.1', description='Custom environments for OpenAI Gym', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-maze-envs', From 2ad112669c312dbbf9111927c30a9c0e64f804f9 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Tue, 6 Feb 2018 14:50:12 +0100 Subject: [PATCH 21/83] Setup file changes --- setup.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/setup.py b/setup.py index 0a334dbe..e6cc17cf 100644 --- a/setup.py +++ b/setup.py @@ -1,11 +1,11 @@ from setuptools import setup, find_packages -setup(name='parrot_envs', +setup(name='parrotprediction-openai-envs', version='0.9.1', description='Custom environments for OpenAI Gym', keywords='acs lcs machine-learning reinforcement-learning openai', - url='https://github.com/ParrotPrediction/openai-maze-envs', - author='Parrot Prediction', + url='https://github.com/ParrotPrediction/openai-envs', + author='Parrot Prediction Ltd.', author_email='contact@parrotprediction.com', license='MIT', packages=find_packages(), From 5d48e004cd343912a5b7033c6b6b07bd71754521 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 19 Feb 2018 15:06:50 +0100 Subject: [PATCH 22/83] Maze4, Maze6, Woods14 added --- gym_maze/__init__.py | 21 +++++++++++++++++++++ gym_maze/envs/Maze4.py | 17 +++++++++++++++++ gym_maze/envs/Maze6.py | 18 ++++++++++++++++++ gym_maze/envs/Woods14.py | 16 ++++++++++++++++ gym_maze/envs/__init__.py | 3 +++ 5 files changed, 75 insertions(+) create mode 100644 gym_maze/envs/Maze4.py create mode 100644 gym_maze/envs/Maze6.py create mode 100644 gym_maze/envs/Woods14.py diff --git a/gym_maze/__init__.py b/gym_maze/__init__.py index 74838c0c..be06cbb6 100644 --- a/gym_maze/__init__.py +++ b/gym_maze/__init__.py @@ -48,10 +48,24 @@ def find_action_by_direction(direction): nondeterministic=True ) +register( + id='Maze4-v0', + entry_point='gym_maze.envs:Maze4', + max_episode_steps=50, + nondeterministic=False +) + register( id='Maze5-v0', entry_point='gym_maze.envs:Maze5', max_episode_steps=50, + nondeterministic=False +) + +register( + id='Maze6-v0', + entry_point='gym_maze.envs:Maze6', + max_episode_steps=50, nondeterministic=True ) @@ -67,4 +81,11 @@ def find_action_by_direction(direction): entry_point='gym_maze.envs:Woods1', max_episode_steps=50, nondeterministic=False +) + +register( + id='Woods14-v0', + entry_point='gym_maze.envs:Woods14', + max_episode_steps=50, + nondeterministic=False ) \ No newline at end of file diff --git a/gym_maze/envs/Maze4.py b/gym_maze/envs/Maze4.py new file mode 100644 index 00000000..c6112a27 --- /dev/null +++ b/gym_maze/envs/Maze4.py @@ -0,0 +1,17 @@ +from gym_maze.envs import AbstractMaze + +import numpy as np + + +class Maze4(AbstractMaze): + def __init__(self): + super().__init__(np.matrix([ + [1, 1, 1, 1, 1, 1, 1, 1], + [1, 0, 0, 1, 0, 0, 9, 1], + [1, 1, 0, 0, 1, 0, 0, 1], + [1, 1, 0, 1, 0, 0, 1, 1], + [1, 0, 0, 0, 0, 0, 0, 1], + [1, 1, 0, 1, 0, 0, 0, 1], + [1, 0, 0, 0, 0, 1, 0, 1], + [1, 1, 1, 1, 1, 1, 1, 1] + ])) diff --git a/gym_maze/envs/Maze6.py b/gym_maze/envs/Maze6.py new file mode 100644 index 00000000..b2241629 --- /dev/null +++ b/gym_maze/envs/Maze6.py @@ -0,0 +1,18 @@ +from gym_maze.envs import AbstractMaze + +import numpy as np + + +class Maze6(AbstractMaze): + def __init__(self): + super().__init__(np.matrix([ + [1, 1, 1, 1, 1, 1, 1, 1, 1], + [1, 0, 0, 0, 0, 0, 1, 9, 1], + [1, 0, 0, 1, 0, 1, 1, 0, 1], + [1, 0, 1, 0, 0, 0, 0, 0, 1], + [1, 0, 0, 0, 1, 1, 0, 0, 1], + [1, 0, 1, 0, 1, 0, 0, 1, 1], + [1, 0, 1, 0, 0, 1, 0, 0, 1], + [1, 0, 0, 0, 0, 0, 0, 0, 1], + [1, 1, 1, 1, 1, 1, 1, 1, 1], + ])) diff --git a/gym_maze/envs/Woods14.py b/gym_maze/envs/Woods14.py new file mode 100644 index 00000000..fc960e2e --- /dev/null +++ b/gym_maze/envs/Woods14.py @@ -0,0 +1,16 @@ +from gym_maze.envs import AbstractMaze + +import numpy as np + + +class Woods14(AbstractMaze): + def __init__(self): + super().__init__(np.matrix([ + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], + [1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1], + [1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1], + [1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1], + [1, 9, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1], + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], + ])) diff --git a/gym_maze/envs/__init__.py b/gym_maze/envs/__init__.py index 9f1adf5f..b687d602 100644 --- a/gym_maze/envs/__init__.py +++ b/gym_maze/envs/__init__.py @@ -3,6 +3,9 @@ from gym_maze.envs.MazeF2 import MazeF2 from gym_maze.envs.MazeF3 import MazeF3 from gym_maze.envs.MazeF4 import MazeF4 +from gym_maze.envs.Maze4 import Maze4 from gym_maze.envs.Maze5 import Maze5 +from gym_maze.envs.Maze6 import Maze6 from gym_maze.envs.BMaze4 import BMaze4 from gym_maze.envs.Woods1 import Woods1 +from gym_maze.envs.Woods14 import Woods14 From 6ab0d2e95416045f70e77cc6c1228a8de2b475be Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 19 Feb 2018 15:42:02 +0100 Subject: [PATCH 23/83] Version bumped --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index e6cc17cf..e12a1a99 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='parrotprediction-openai-envs', - version='0.9.1', + version='0.9.2', description='Custom environments for OpenAI Gym', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-envs', From b441b4445168470f539e5f78dd8bf78d07278bdb Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 5 Mar 2018 13:38:56 +0100 Subject: [PATCH 24/83] Add bigger multiplexer problems --- gym_multiplexer/__init__.py | 17 +++++++++++++++++ setup.py | 2 +- 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/gym_multiplexer/__init__.py b/gym_multiplexer/__init__.py index 4a79dca0..706b165c 100644 --- a/gym_multiplexer/__init__.py +++ b/gym_multiplexer/__init__.py @@ -5,6 +5,9 @@ name = "boolean-multiplexer" max_episode_steps = 1 +# Length of a multiplexer is calculated +# using l = k + 2^k + register( id='{}-3bit-v0'.format(name), entry_point='gym_multiplexer:BooleanMultiplexer', @@ -24,4 +27,18 @@ entry_point='gym_multiplexer:BooleanMultiplexer', max_episode_steps=max_episode_steps, kwargs={'control_bits': 3} +) + +register( + id='{}-20bit-v0'.format(name), + entry_point='gym_multiplexer:BooleanMultiplexer', + max_episode_steps=max_episode_steps, + kwargs={'control_bits': 4} +) + +register( + id='{}-37bit-v0'.format(name), + entry_point='gym_multiplexer:BooleanMultiplexer', + max_episode_steps=max_episode_steps, + kwargs={'control_bits': 5} ) \ No newline at end of file diff --git a/setup.py b/setup.py index e12a1a99..5da9a38a 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='parrotprediction-openai-envs', - version='0.9.2', + version='0.9.3', description='Custom environments for OpenAI Gym', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-envs', From 92a83c25c5cc6d2043aef7407765462e0856af10 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 19 Mar 2018 10:55:52 +0100 Subject: [PATCH 25/83] RC --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 5da9a38a..6078e3c0 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='parrotprediction-openai-envs', - version='0.9.3', + version='1.0.0', description='Custom environments for OpenAI Gym', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-envs', From a36dc342582de536be4854c465c4565ac8ec8a1c Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 19 Mar 2018 11:02:16 +0100 Subject: [PATCH 26/83] Fixes to Travis config file --- .travis.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 91e25df4..0e5a92df 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,10 +1,9 @@ language: python python: - - '3.5' + - '3.6' script: - - touch foo - pip3 install -r requirements.txt - pytest From 80d7b24af48ca0b5bb861013cbb82d106e744573 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Mon, 19 Mar 2018 11:14:41 +0100 Subject: [PATCH 27/83] Skip cleanup in travis --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 0e5a92df..9bf7cc06 100644 --- a/.travis.yml +++ b/.travis.yml @@ -8,6 +8,7 @@ script: - pytest deploy: + skip_cleanup: true provider: pypi user: khozzy password: From 5a3d6f49d7c56ab05709a747114d7d62111327b7 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Tue, 26 Jun 2018 15:27:39 +0200 Subject: [PATCH 28/83] Real Multiplexer --- gym_multiplexer/__init__.py | 32 ++++++--- gym_multiplexer/boolean_multiplexer.py | 54 ++------------- gym_multiplexer/multiplexer.py | 57 ++++++++++++++++ gym_multiplexer/real_multiplexer.py | 15 ++++ ...iplexer.py => test_boolean_multiplexer.py} | 14 ++-- .../tests/test_real_multiplexer.py | 68 +++++++++++++++++++ gym_multiplexer/tests/test_utils.py | 8 +-- gym_multiplexer/utils/utils.py | 5 +- setup.py | 2 +- 9 files changed, 183 insertions(+), 72 deletions(-) create mode 100644 gym_multiplexer/multiplexer.py create mode 100644 gym_multiplexer/real_multiplexer.py rename gym_multiplexer/tests/{test_multiplexer.py => test_boolean_multiplexer.py} (88%) create mode 100644 gym_multiplexer/tests/test_real_multiplexer.py diff --git a/gym_multiplexer/__init__.py b/gym_multiplexer/__init__.py index 706b165c..ebab56ad 100644 --- a/gym_multiplexer/__init__.py +++ b/gym_multiplexer/__init__.py @@ -1,44 +1,60 @@ -from .boolean_multiplexer import BooleanMultiplexer - from gym.envs.registration import register -name = "boolean-multiplexer" +from .boolean_multiplexer import BooleanMultiplexer +from .real_multiplexer import RealMultiplexer + +bool_mpx_name = "boolean-multiplexer" +real_mpx_name = "real-multiplexer" max_episode_steps = 1 # Length of a multiplexer is calculated # using l = k + 2^k register( - id='{}-3bit-v0'.format(name), + id='{}-3bit-v0'.format(bool_mpx_name), entry_point='gym_multiplexer:BooleanMultiplexer', max_episode_steps=max_episode_steps, kwargs={'control_bits': 1} ) register( - id='{}-6bit-v0'.format(name), + id='{}-6bit-v0'.format(bool_mpx_name), entry_point='gym_multiplexer:BooleanMultiplexer', max_episode_steps=max_episode_steps, kwargs={'control_bits': 2} ) register( - id='{}-11bit-v0'.format(name), + id='{}-11bit-v0'.format(bool_mpx_name), entry_point='gym_multiplexer:BooleanMultiplexer', max_episode_steps=max_episode_steps, kwargs={'control_bits': 3} ) register( - id='{}-20bit-v0'.format(name), + id='{}-20bit-v0'.format(bool_mpx_name), entry_point='gym_multiplexer:BooleanMultiplexer', max_episode_steps=max_episode_steps, kwargs={'control_bits': 4} ) register( - id='{}-37bit-v0'.format(name), + id='{}-37bit-v0'.format(bool_mpx_name), entry_point='gym_multiplexer:BooleanMultiplexer', max_episode_steps=max_episode_steps, kwargs={'control_bits': 5} +) + +register( + id='{}-3bit-v0'.format(real_mpx_name), + entry_point='gym_multiplexer:RealMultiplexer', + max_episode_steps=max_episode_steps, + kwargs={'control_bits': 1} +) + +register( + id='{}-6bit-v0'.format(real_mpx_name), + entry_point='gym_multiplexer:RealMultiplexer', + max_episode_steps=max_episode_steps, + kwargs={'control_bits': 2} ) \ No newline at end of file diff --git a/gym_multiplexer/boolean_multiplexer.py b/gym_multiplexer/boolean_multiplexer.py index d9c0c6f7..e257a7e9 100644 --- a/gym_multiplexer/boolean_multiplexer.py +++ b/gym_multiplexer/boolean_multiplexer.py @@ -1,58 +1,14 @@ -import logging -import random - -import gym -from bitstring import BitArray from gym.spaces import Discrete +from .multiplexer import Multiplexer -class BooleanMultiplexer(gym.Env): - REWARD = 1000 +class BooleanMultiplexer(Multiplexer): def __init__(self, control_bits=3) -> None: - self.control_bits = control_bits - self.metadata = {'render.modes': ['human']} + super().__init__(control_bits) self.observation_space = Discrete(self._observation_string_length) self.action_space = Discrete(2) - def _reset(self): - logging.debug("Resetting the environment") - bits = BitArray([random.randint(0, 1) for _ in - range(0, self._observation_string_length - 1)]) - - self._ctrl_bits = bits[:self.control_bits] - self._data_bits = bits[self.control_bits:] - self._validation_bit = False - - return self._observation() - - def _step(self, action): - reward = 0 - - if action == self._answer: - self._validation_bit = True - reward = self.REWARD - - return self._observation(), reward, None, None - - def _render(self, mode='human', close=False): - if close: - return - - if mode == 'human': - return self._observation() - else: - super(BooleanMultiplexer, self).render(mode=mode) - - def _observation(self) -> str: - return (self._ctrl_bits + self._data_bits - + BitArray([self._validation_bit])).bin - - @property - def _observation_string_length(self): - return self.control_bits + pow(2, self.control_bits) + 1 - - @property - def _answer(self): - return int(self._data_bits[self._ctrl_bits.uint]) + def _internal_state(self): + return map(lambda x: round(x), self._state) diff --git a/gym_multiplexer/multiplexer.py b/gym_multiplexer/multiplexer.py new file mode 100644 index 00000000..e1d0c4e2 --- /dev/null +++ b/gym_multiplexer/multiplexer.py @@ -0,0 +1,57 @@ +import random + +import gym + +from .utils import get_correct_answer + + +class Multiplexer(gym.Env): + + REWARD = 1000 + + def _internal_state(self): raise NotImplementedError + + def __init__(self, control_bits=3) -> None: + self.control_bits = control_bits + self.metadata = {'render.modes': ['human']} + + self._state = None + self._validation_bit = 0 + + def _reset(self): + self._state = [random.random() for _ in + range(0, self._observation_string_length - 1)] + self._validation_bit = 0 + return self._observation + + def _step(self, action): + reward = 0 + + if action == self._correct_answer: + self._validation_bit = 1 + reward = self.REWARD + + return self._observation, reward, None, None + + def _render(self, mode='human', close=False): + if close: + return + + if mode == 'human': + return self._observation + + return self.render(mode=mode) + + @property + def _observation(self) -> list: + observation = list(self._state) + observation.append(self._validation_bit) + return observation + + @property + def _correct_answer(self): + return get_correct_answer(list(self._internal_state()) , self.control_bits) + + @property + def _observation_string_length(self): + return self.control_bits + pow(2, self.control_bits) + 1 \ No newline at end of file diff --git a/gym_multiplexer/real_multiplexer.py b/gym_multiplexer/real_multiplexer.py new file mode 100644 index 00000000..5d51fd11 --- /dev/null +++ b/gym_multiplexer/real_multiplexer.py @@ -0,0 +1,15 @@ +from gym.spaces import Box, Discrete + +from .multiplexer import Multiplexer + + +class RealMultiplexer(Multiplexer): + + def __init__(self, control_bits=3, threshold=.5) -> None: + super().__init__(control_bits) + self.threshold = threshold + self.observation_space = Box(low=0, high=1, shape=(self._observation_string_length, )) + self.action_space = Discrete(2) + + def _internal_state(self): + return map(lambda x: x > self.threshold, self._state) diff --git a/gym_multiplexer/tests/test_multiplexer.py b/gym_multiplexer/tests/test_boolean_multiplexer.py similarity index 88% rename from gym_multiplexer/tests/test_multiplexer.py rename to gym_multiplexer/tests/test_boolean_multiplexer.py index 689cc2ba..42ae3e9b 100644 --- a/gym_multiplexer/tests/test_multiplexer.py +++ b/gym_multiplexer/tests/test_boolean_multiplexer.py @@ -10,7 +10,7 @@ logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) -class TestMultiplexer: +class TestBooleanMultiplexer: def test_should_initialize_multiplexer(self): # when mp = gym.make('boolean-multiplexer-6bit-v0') @@ -29,9 +29,9 @@ def test_should_return_observation_when_reset(self): # then assert state is not None - assert state[-1] == '0' + assert state[-1] == 0 assert 7 == len(state) - assert type(state) is str + assert type(state) is list def test_should_render_state(self): # given @@ -43,9 +43,9 @@ def test_should_render_state(self): # then assert state is not None - assert state[-1] == '0' + assert state[-1] == 0 assert 4 == len(state) - assert type(state) is str + assert type(state) is list def test_should_execute_step(self): # given @@ -58,8 +58,8 @@ def test_should_execute_step(self): # then assert state is not None - assert state[-1] in ['0', '1'] - assert type(state) is str + assert state[-1] in [0, 1] + assert type(state) is list assert reward in [0, 1000] assert done is True diff --git a/gym_multiplexer/tests/test_real_multiplexer.py b/gym_multiplexer/tests/test_real_multiplexer.py new file mode 100644 index 00000000..b6ab8517 --- /dev/null +++ b/gym_multiplexer/tests/test_real_multiplexer.py @@ -0,0 +1,68 @@ +import logging +import random +import sys + +import gym + +# noinspection PyUnresolvedReferences +import gym_multiplexer + +logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) + +class TestRealMultiplexer: + + def test_should_initialize_real_mpx(self): + # when + mp = gym.make("real-multiplexer-6bit-v0") + + # then + assert mp is not None + assert (7,) == mp.observation_space.shape + assert 2 == mp.action_space.n + + def test_should_return_observation_when_reset(self): + # given + mp = gym.make('real-multiplexer-6bit-v0') + + # when + state = mp.reset() + + # then + assert state is not None + assert state[-1] == 0 + assert 7 == len(state) + assert type(state) is list + + def test_should_execute_step(self): + # given + mp = gym.make('real-multiplexer-6bit-v0') + mp.reset() + action = self._random_action() + + # when + state, reward, done, _ = mp.step(action) + + # then + assert state is not None + assert state[-1] in [0, 1] + assert type(state) is list + assert reward in [0, 1000] + assert done is True + + def test_execute_multiple_steps_and_keep_constant_perception_length(self): + # given + mp = gym.make('real-multiplexer-6bit-v0') + steps = 100 + + # when & then + for _ in range(0, steps): + p0 = mp.reset() + assert 7 == len(p0) + + action = self._random_action() + p1, reward, done, _ = mp.step(action) + assert 7 == len(p1) + + @staticmethod + def _random_action(): + return random.sample([0, 1], 1)[0] \ No newline at end of file diff --git a/gym_multiplexer/tests/test_utils.py b/gym_multiplexer/tests/test_utils.py index fe47d2c6..f000de16 100644 --- a/gym_multiplexer/tests/test_utils.py +++ b/gym_multiplexer/tests/test_utils.py @@ -4,11 +4,11 @@ class TestUtils: def test_should_calculate_correct_answer_for_3bit_multiplexer(self): - assert 1 == get_correct_answer('0100', 1) - assert 0 == get_correct_answer('1100', 1) + assert 1 == get_correct_answer([0,1,0,0], 1) + assert 0 == get_correct_answer([1,1,0,0], 1) def test_should_calculate_correct_answer_for_6bit_multiplexer(self): - assert 0 == get_correct_answer('1101000', 2) + assert 0 == get_correct_answer([1,1,0,1,0,0,0], 2) def test_should_calculate_correct_answer_for_11bit_multiplexer(self): - assert 1 == get_correct_answer('1011011010', 3) + assert 1 == get_correct_answer([1,0,1,1,0,1,1,0,1,0], 3) diff --git a/gym_multiplexer/utils/utils.py b/gym_multiplexer/utils/utils.py index ecbf15a4..a243fe4f 100644 --- a/gym_multiplexer/utils/utils.py +++ b/gym_multiplexer/utils/utils.py @@ -1,11 +1,10 @@ from bitstring import BitString -def get_correct_answer(bitstring: str, control_bits: int) -> int: - bits = BitString(bin=bitstring) +def get_correct_answer(bitstring: list, control_bits: int) -> int: + bits = BitString(bitstring) _ctrl_bits = bits[:control_bits] _data_bits = bits[control_bits:] - _validation_bit = bits[-1] return int(_data_bits[_ctrl_bits.uint]) diff --git a/setup.py b/setup.py index 6078e3c0..1017ee02 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='parrotprediction-openai-envs', - version='1.0.0', + version='2.0.0', description='Custom environments for OpenAI Gym', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-envs', From f42c44068b14d3e935e6e299b173935e30d8d4b1 Mon Sep 17 00:00:00 2001 From: Norbert Kozlowski Date: Tue, 26 Jun 2018 18:23:47 +0200 Subject: [PATCH 29/83] Fix boolean MPX states --- gym_multiplexer/boolean_multiplexer.py | 7 ++++++- gym_multiplexer/multiplexer.py | 4 ++-- gym_multiplexer/real_multiplexer.py | 7 ++++++- gym_multiplexer/tests/test_boolean_multiplexer.py | 5 ++++- setup.py | 2 +- 5 files changed, 19 insertions(+), 6 deletions(-) diff --git a/gym_multiplexer/boolean_multiplexer.py b/gym_multiplexer/boolean_multiplexer.py index e257a7e9..fecf09ec 100644 --- a/gym_multiplexer/boolean_multiplexer.py +++ b/gym_multiplexer/boolean_multiplexer.py @@ -1,3 +1,5 @@ +import random + from gym.spaces import Discrete from .multiplexer import Multiplexer @@ -10,5 +12,8 @@ def __init__(self, control_bits=3) -> None: self.observation_space = Discrete(self._observation_string_length) self.action_space = Discrete(2) + def _generate_state(self): + return [random.randint(0, 1) for _ in range(0, self._observation_string_length - 1)] + def _internal_state(self): - return map(lambda x: round(x), self._state) + return list(map(lambda x: round(x), self._state)) diff --git a/gym_multiplexer/multiplexer.py b/gym_multiplexer/multiplexer.py index e1d0c4e2..06b4ddce 100644 --- a/gym_multiplexer/multiplexer.py +++ b/gym_multiplexer/multiplexer.py @@ -9,6 +9,7 @@ class Multiplexer(gym.Env): REWARD = 1000 + def _generate_state(self): raise NotImplementedError def _internal_state(self): raise NotImplementedError def __init__(self, control_bits=3) -> None: @@ -19,8 +20,7 @@ def __init__(self, control_bits=3) -> None: self._validation_bit = 0 def _reset(self): - self._state = [random.random() for _ in - range(0, self._observation_string_length - 1)] + self._state = self._generate_state() self._validation_bit = 0 return self._observation diff --git a/gym_multiplexer/real_multiplexer.py b/gym_multiplexer/real_multiplexer.py index 5d51fd11..08e1d38c 100644 --- a/gym_multiplexer/real_multiplexer.py +++ b/gym_multiplexer/real_multiplexer.py @@ -1,3 +1,5 @@ +import random + from gym.spaces import Box, Discrete from .multiplexer import Multiplexer @@ -11,5 +13,8 @@ def __init__(self, control_bits=3, threshold=.5) -> None: self.observation_space = Box(low=0, high=1, shape=(self._observation_string_length, )) self.action_space = Discrete(2) + def _generate_state(self): + return [random.random() for _ in range(0, self._observation_string_length - 1)] + def _internal_state(self): - return map(lambda x: x > self.threshold, self._state) + return list(map(lambda x: x > self.threshold, self._state)) diff --git a/gym_multiplexer/tests/test_boolean_multiplexer.py b/gym_multiplexer/tests/test_boolean_multiplexer.py index 42ae3e9b..c50c4f36 100644 --- a/gym_multiplexer/tests/test_boolean_multiplexer.py +++ b/gym_multiplexer/tests/test_boolean_multiplexer.py @@ -32,6 +32,8 @@ def test_should_return_observation_when_reset(self): assert state[-1] == 0 assert 7 == len(state) assert type(state) is list + for obs in state: + assert obs in [0, 1] def test_should_render_state(self): # given @@ -58,10 +60,11 @@ def test_should_execute_step(self): # then assert state is not None - assert state[-1] in [0, 1] assert type(state) is list assert reward in [0, 1000] assert done is True + for obs in state: + assert obs in [0, 1] def test_execute_multiple_steps_and_keep_constant_perception_length(self): # given diff --git a/setup.py b/setup.py index 1017ee02..f4d7fc02 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ from setuptools import setup, find_packages setup(name='parrotprediction-openai-envs', - version='2.0.0', + version='2.0.1', description='Custom environments for OpenAI Gym', keywords='acs lcs machine-learning reinforcement-learning openai', url='https://github.com/ParrotPrediction/openai-envs', From 7613852f3467b65e0a76dd2b6b413e869ebb0a15 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 4 Jul 2018 22:13:04 +0200 Subject: [PATCH 30/83] added function _run_action_planning but it doesn't work yet --- alcs/acs2/ACS2.py | 21 +++++++++++++++++++++ alcs/acs2/ClassifiersList.py | 19 +++++++++++++++++++ 2 files changed, 40 insertions(+) diff --git a/alcs/acs2/ACS2.py b/alcs/acs2/ACS2.py index 01cc0a30..62ed818b 100644 --- a/alcs/acs2/ACS2.py +++ b/alcs/acs2/ACS2.py @@ -33,6 +33,7 @@ def explore_exploit(self, env, max_trials): :param max_trials: number of trials :return: population of classifiers and metrics """ + def switch_phases(env, steps, current_trial): if current_trial % 2 == 0: return self._run_trial_explore(env, steps) @@ -172,6 +173,26 @@ def _run_trial_exploit(self, env, time=None, current_trial=None): return steps + def _run_action_planning(self, env, time, situation, + previous_situation, actionSet, act, rho0): + steps = 0 + matchSet = 0 # TODO ClassifierList *matchSet = 0; + done = False + goal_situation = 0 # TODO env->getGoalState(goalSituation) + + while not done: + # TODO if (!env->getGoalState(goalSituation)) {break; + act_sequence = self.population.search_goal_sequence(situation, goal_situation) + i = 0 + while act_sequence[i] != 0: + matchSet = ClassifiersList() #TODO new constructor??? + + i += 1 + pass + pass + + return steps + def _parse_state(self, raw_state): """ Sometimes the environment state returned by the OpenAI diff --git a/alcs/acs2/ClassifiersList.py b/alcs/acs2/ClassifiersList.py index 2466659b..c54df3d1 100644 --- a/alcs/acs2/ClassifiersList.py +++ b/alcs/acs2/ClassifiersList.py @@ -19,6 +19,21 @@ def __init__(self, seq=(), cfg=None): self.cfg = cfg list.__init__(self, seq or []) + #TODO possibly new constructor needed????? + # /** + # * Constructor for match set generation. (Does not copy classifiers.) + # */ + # ClassifierList::ClassifierList(ClassifierList *pop, Perception *percept) { + # list = 0; + # size = 0; + # env = pop->env; + # for (PureClassifierList *listp = pop->list; listp != 0; listp = listp->next) { + # if (listp->cl->doesMatch(percept)) { + # addClassifier(listp->cl); + # } + # } + # } + def append(self, item): if not isinstance(item, Classifier): raise TypeError("Item should be a Classifier object") @@ -514,3 +529,7 @@ def find_subsumer(self, cl: Classifier, choice_func=choice) -> Classifier: return choice_func(most_general_subsumers) \ if most_general_subsumers else None + + def search_goal_sequence(self, start, goal): + #TODO: Action **ClassifierList::searchGoalSequence(Perception *start, Perception *goal) { + pass From e1723935a0c7bae4b63a1bb939ba7b37ded8caba Mon Sep 17 00:00:00 2001 From: e-dzia Date: Sun, 8 Jul 2018 15:03:07 +0200 Subject: [PATCH 31/83] added explanatory comments --- assets/ACS2/acs2++.cc | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/assets/ACS2/acs2++.cc b/assets/ACS2/acs2++.cc index cee2dd1f..ff19e212 100755 --- a/assets/ACS2/acs2++.cc +++ b/assets/ACS2/acs2++.cc @@ -298,6 +298,7 @@ int startOneTrialExploit(ClassifierList *population, Environment *env) { int startActionPlanning(ClassifierList *population, Environment *env, int time, ofstream *out, Perception *situation, Perception *previousSituation, ClassifierList **actionSet, Action *act, double *rho0) { /* Recheck this function -> is step set correct and we need to do model testing in here! */ + //Perception - Perception *goalSituation = new Perception(); int steps; @@ -306,7 +307,9 @@ int startActionPlanning(ClassifierList *population, Environment *env, int time, for (steps = 0; !env->isReset() && (REWARD_TEST || time + steps <= MAX_STEPS) && (!REWARD_TEST || steps < MAX_TRIAL_STEPS);) { + //take new goal from environment if (!env->getGoalState(goalSituation)) { + //if there is none,then break break; } else { Action **actSequence = population->searchGoalSequence(situation, goalSituation); @@ -319,6 +322,7 @@ int startActionPlanning(ClassifierList *population, Environment *env, int time, //Execute the found sequence and learn during executing for (i = 0; actSequence[i] != 0; i++, steps++) { + matchSet = new ClassifierList(population, situation); if ((*actionSet) != 0) { From 950870f928d7e3f5c90f45dd18c6fec73b463030 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Tue, 28 Aug 2018 09:13:40 +0200 Subject: [PATCH 32/83] added handeye env (not finished) TODO: calculate_performance --- integration/handeye/__init__.py | 0 integration/handeye/acs2_in_handeye.py | 38 ++++++++++++++++++++++++++ integration/handeye/utils.py | 32 ++++++++++++++++++++++ 3 files changed, 70 insertions(+) create mode 100644 integration/handeye/__init__.py create mode 100644 integration/handeye/acs2_in_handeye.py create mode 100644 integration/handeye/utils.py diff --git a/integration/handeye/__init__.py b/integration/handeye/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/integration/handeye/acs2_in_handeye.py b/integration/handeye/acs2_in_handeye.py new file mode 100644 index 00000000..5178e01e --- /dev/null +++ b/integration/handeye/acs2_in_handeye.py @@ -0,0 +1,38 @@ +import logging + +import gym + +import sys +sys.path.append('/home/e-dzia/openai-envs') + +# noinspection PyUnresolvedReferences +import gym_handeye +from alcs import ACS2, ACS2Configuration +from integration.handeye.utils import calculate_performance + +# Configure logger +logging.basicConfig(level=logging.INFO) + + +if __name__ == '__main__': + + # Load desired environment + hand_eye = gym.make('HandEye3-v0') + + # Configure and create the agent + cfg = ACS2Configuration(10, 6, + epsilon=1.0, + do_ga=False, + performance_fcn=calculate_performance) + logging.info(cfg) + + # Explore the environment + agent = ACS2(cfg) + population, explore_metrics = agent.explore(hand_eye, 50) + + # Exploit the environment + agent = ACS2(cfg, population) + population, exploit_metric = agent.exploit(hand_eye, 10) + + for metric in exploit_metric: + logging.info(metric) diff --git a/integration/handeye/utils.py b/integration/handeye/utils.py new file mode 100644 index 00000000..a9e7e289 --- /dev/null +++ b/integration/handeye/utils.py @@ -0,0 +1,32 @@ +def calculate_performance(maze, population): + """ + Analyzes all possible transition in maze environment and checks if there + is a reliable classifier for it. + :param maze: maze object + :param population: list of classifiers + :return: percentage of knowledge + """ + # transitions = maze.env.get_all_possible_transitions() + # + # # Take into consideration only reliable classifiers + # reliable_classifiers = [c for c in population if c.is_reliable()] + # + # # Count how many transitions are anticipated correctly + # nr_correct = 0 + # + # # For all possible destinations from each path cell + # for start, action, end in transitions: + # + # p0 = maze.env.maze.perception(*start) + # p1 = maze.env.maze.perception(*end) + # + # if any([True for cl in reliable_classifiers + # if cl.predicts_successfully(p0, action, p1)]): + # nr_correct += 1 + # + # return { + # 'knowledge': nr_correct / len(transitions) * 100.0 + # } + return { + 'knowledge': 1 + } From 4eb972b5d3b982e95ec09990ce9ebdc2a0d63a06 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 29 Aug 2018 16:07:07 +0200 Subject: [PATCH 33/83] changed calculate_performance function But it doesn't work well still :/ --- integration/handeye/utils.py | 45 +++++++++++++++++------------------- 1 file changed, 21 insertions(+), 24 deletions(-) diff --git a/integration/handeye/utils.py b/integration/handeye/utils.py index a9e7e289..781cff31 100644 --- a/integration/handeye/utils.py +++ b/integration/handeye/utils.py @@ -1,32 +1,29 @@ -def calculate_performance(maze, population): +def calculate_performance(hand_eye, population): """ Analyzes all possible transition in maze environment and checks if there is a reliable classifier for it. - :param maze: maze object + :param hand_eye: maze object :param population: list of classifiers :return: percentage of knowledge """ - # transitions = maze.env.get_all_possible_transitions() - # - # # Take into consideration only reliable classifiers - # reliable_classifiers = [c for c in population if c.is_reliable()] - # - # # Count how many transitions are anticipated correctly - # nr_correct = 0 - # - # # For all possible destinations from each path cell - # for start, action, end in transitions: - # - # p0 = maze.env.maze.perception(*start) - # p1 = maze.env.maze.perception(*end) - # - # if any([True for cl in reliable_classifiers - # if cl.predicts_successfully(p0, action, p1)]): - # nr_correct += 1 - # - # return { - # 'knowledge': nr_correct / len(transitions) * 100.0 - # } + transitions = hand_eye.env.get_all_possible_transitions() + + # Take into consideration only reliable classifiers + reliable_classifiers = [c for c in population if c.is_reliable()] + + # Count how many transitions are anticipated correctly + nr_correct = 0 + + # For all possible destinations from each path cell + for start, action, end in transitions: + + p0 = start + p1 = end + + if any([True for cl in reliable_classifiers + if cl.predicts_successfully(p0, action, p1)]): + nr_correct += 1 + return { - 'knowledge': 1 + 'knowledge': nr_correct / len(transitions) * 100.0 } From c9b3413a8f37c2165887e6b59ed3b0fc9f213686 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Thu, 30 Aug 2018 13:03:39 +0200 Subject: [PATCH 34/83] Revert "failed merge" This reverts commit d1681c56a6fb2a8cd12bb7d64f17448bec048d7a, reversing changes made to 4eb972b5d3b982e95ec09990ce9ebdc2a0d63a06. --- .gitignore | 31 +-- .travis.yml | 17 -- README.md | 17 -- assets/ACS2/ACSConstants.h | 7 +- assets/ACS2/acs2++.cc | 2 - examples/maze.py | 33 ---- gym_maze/__init__.py | 91 --------- gym_maze/envs/BMaze4.py | 17 -- gym_maze/envs/Maze4.py | 17 -- gym_maze/envs/Maze5.py | 18 -- gym_maze/envs/Maze6.py | 18 -- gym_maze/envs/MazeF1.py | 15 -- gym_maze/envs/MazeF2.py | 15 -- gym_maze/envs/MazeF3.py | 15 -- gym_maze/envs/MazeF4.py | 15 -- gym_maze/envs/Woods1.py | 14 -- gym_maze/envs/Woods14.py | 16 -- gym_maze/envs/__init__.py | 11 -- gym_maze/envs/abstract_maze.py | 145 --------------- gym_maze/maze.py | 176 ------------------ gym_maze/tests/__init__.py | 0 gym_maze/tests/test_utils.py | 15 -- gym_maze/utils/__init__.py | 1 - gym_maze/utils/utils.py | 58 ------ gym_multiplexer/__init__.py | 60 ------ gym_multiplexer/boolean_multiplexer.py | 19 -- gym_multiplexer/multiplexer.py | 57 ------ gym_multiplexer/real_multiplexer.py | 20 -- gym_multiplexer/tests/__init__.py | 0 .../tests/test_boolean_multiplexer.py | 85 --------- .../tests/test_real_multiplexer.py | 68 ------- gym_multiplexer/tests/test_utils.py | 14 -- gym_multiplexer/utils/__init__.py | 1 - gym_multiplexer/utils/utils.py | 10 - requirements.txt | 11 +- setup.py | 31 ++- 36 files changed, 51 insertions(+), 1089 deletions(-) delete mode 100644 examples/maze.py delete mode 100644 gym_maze/__init__.py delete mode 100644 gym_maze/envs/BMaze4.py delete mode 100644 gym_maze/envs/Maze4.py delete mode 100644 gym_maze/envs/Maze5.py delete mode 100644 gym_maze/envs/Maze6.py delete mode 100644 gym_maze/envs/MazeF1.py delete mode 100644 gym_maze/envs/MazeF2.py delete mode 100644 gym_maze/envs/MazeF3.py delete mode 100644 gym_maze/envs/MazeF4.py delete mode 100644 gym_maze/envs/Woods1.py delete mode 100644 gym_maze/envs/Woods14.py delete mode 100644 gym_maze/envs/__init__.py delete mode 100644 gym_maze/envs/abstract_maze.py delete mode 100644 gym_maze/maze.py delete mode 100644 gym_maze/tests/__init__.py delete mode 100644 gym_maze/tests/test_utils.py delete mode 100644 gym_maze/utils/__init__.py delete mode 100644 gym_maze/utils/utils.py delete mode 100644 gym_multiplexer/__init__.py delete mode 100644 gym_multiplexer/boolean_multiplexer.py delete mode 100644 gym_multiplexer/multiplexer.py delete mode 100644 gym_multiplexer/real_multiplexer.py delete mode 100644 gym_multiplexer/tests/__init__.py delete mode 100644 gym_multiplexer/tests/test_boolean_multiplexer.py delete mode 100644 gym_multiplexer/tests/test_real_multiplexer.py delete mode 100644 gym_multiplexer/tests/test_utils.py delete mode 100644 gym_multiplexer/utils/__init__.py delete mode 100644 gym_multiplexer/utils/utils.py diff --git a/.gitignore b/.gitignore index f086afb0..7126877d 100644 --- a/.gitignore +++ b/.gitignore @@ -20,7 +20,6 @@ lib64/ parts/ sdist/ var/ -wheels/ *.egg-info/ .installed.cfg *.egg @@ -41,9 +40,10 @@ htmlcov/ .coverage .coverage.* .cache +.pytest_cache nosetests.xml coverage.xml -*.cover +*,cover .hypothesis/ # Translations @@ -67,7 +67,7 @@ docs/_build/ # PyBuilder target/ -# Jupyter Notebook +# IPython Notebook .ipynb_checkpoints # pyenv @@ -76,28 +76,33 @@ target/ # celery beat schedule file celerybeat-schedule -# SageMath parsed files -*.sage.py - # dotenv .env # virtualenv -.venv venv/ ENV/ # Spyder project settings .spyderproject -.spyproject # Rope project settings .ropeproject -# mkdocs documentation -/site - -# mypy -.mypy_cache/ .idea +/examples/learning_progress.png +*.pkl + .DS_Store +/assets/ACS2/cmake-build-debug/ +/assets/ACS2/*.txt + +/.coverage + +# C++ byproducts +*.o +*.out + +# Not wanted, EPS graphics, serialized pickle dumps +*.eps +*.p diff --git a/.travis.yml b/.travis.yml index 96c18b15..177b0762 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,5 +1,4 @@ language: python -<<<<<<< HEAD python: - '3.6' before_install: @@ -13,22 +12,6 @@ deploy: user: khozzy password: secure: 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 -======= - -python: - - '3.6' - -script: - - pip3 install -r requirements.txt - - pytest - -deploy: - skip_cleanup: true - provider: pypi - user: khozzy - password: - secure: 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 ->>>>>>> f42c44068b14d3e935e6e299b173935e30d8d4b1 on: tags: true branch: master diff --git a/README.md b/README.md index e63173ec..4f6c35b5 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,3 @@ -<<<<<<< HEAD # Anticipatory Learning Classifier Systems in Python Repository containing code implementation for various *Anticipatory Learning Classifier Systems* (ALCS). @@ -85,19 +84,3 @@ Prior to PR please execute to check if standards are holding: make test make coverage make pep8 -======= -# Parrot Prediction OpenAI environments - -## Maze - -Initializing - - maze = gym.make('MazeF1-v0') - -Getting all possible transitions - - transitions = maze.env.get_all_possible_transitions() - -## Boolean Multiplexer -Read blog [post](https://medium.com/parrot-prediction/boolean-multiplexer-in-practice-94e3236821b5) describing the usage. ->>>>>>> f42c44068b14d3e935e6e299b173935e30d8d4b1 diff --git a/assets/ACS2/ACSConstants.h b/assets/ACS2/ACSConstants.h index 98adf171..5b9021d7 100755 --- a/assets/ACS2/ACSConstants.h +++ b/assets/ACS2/ACSConstants.h @@ -41,8 +41,7 @@ #define PROB_EXPLORATION_BIAS 0.5 /* specifies the probability of applying an exploration biased action-selection */ #define EXPLORATION_BIAS_METHOD 2 /* 0 = action delay bias, 1 = knowledge array bias, 2 = 50/50 */ -/*********************ACTION PLANNING***************************/ -#define DO_ACTION_PLANNING 1 +#define DO_ACTION_PLANNING 0 #define ACTION_PLANNING_FREQUENCY 50 /* GA constants */ @@ -57,8 +56,8 @@ #define DO_SUBSUMPTION 1 /*andere Makros:*/ -#define ENVIRONMENT_CLASS HandEyeEnvironment /*MPEnvironment*/ -#define RESULT_FILE "ACS2_tests.txt" /*"ACS2_Maze4_5050B050.txt"*/ +#define ENVIRONMENT_CLASS MPEnvironment +#define RESULT_FILE "ACS2_Maze4_5050B050.txt" #define MAX_STEPS 30000 #define MAX_TRIAL_STEPS 50 diff --git a/assets/ACS2/acs2++.cc b/assets/ACS2/acs2++.cc index b99dacb1..ff19e212 100755 --- a/assets/ACS2/acs2++.cc +++ b/assets/ACS2/acs2++.cc @@ -24,7 +24,6 @@ #include "ClassifierList.h" #include "MazeEnvironment.h" #include "MPEnvironment.h" -#include "HandEyeEnvironment.h" using namespace std; @@ -300,7 +299,6 @@ int startActionPlanning(ClassifierList *population, Environment *env, int time, Perception *previousSituation, ClassifierList **actionSet, Action *act, double *rho0) { /* Recheck this function -> is step set correct and we need to do model testing in here! */ //Perception - - //cout << "############Action Planning##########################################\n"; Perception *goalSituation = new Perception(); int steps; diff --git a/examples/maze.py b/examples/maze.py deleted file mode 100644 index 0d9d24d0..00000000 --- a/examples/maze.py +++ /dev/null @@ -1,33 +0,0 @@ -import logging -from random import choice - -import gym - -# noinspection PyUnresolvedReferences -import gym_maze - -logging.basicConfig(level=logging.DEBUG) - -if __name__ == '__main__': - maze = gym.make('MazeF1-v0') - - possible_actions = list(range(8)) - transitions = maze.env.get_all_possible_transitions() - - for i_episode in range(1): - observation = maze.reset() - - for t in range(100): - logging.info("Time: [{}], observation: [{}]".format(t, observation)) - - action = choice(possible_actions) - - logging.info("\t\tExecuted action: [{}]".format(action)) - observation, reward, done, info = maze.step(action) - - if done: - logging.info("Episode finished after {} timesteps.".format(t + 1)) - logging.info("Last reward: {}".format(reward)) - break - - logging.info("Finished") diff --git a/gym_maze/__init__.py b/gym_maze/__init__.py deleted file mode 100644 index be06cbb6..00000000 --- a/gym_maze/__init__.py +++ /dev/null @@ -1,91 +0,0 @@ -from gym.envs.registration import register - -# noinspection PyUnresolvedReferences -from .maze import Maze, PATH_MAPPING, WALL_MAPPING, REWARD_MAPPING - -ACTION_LOOKUP = { - 0: 'N', - 1: 'NE', - 2: 'E', - 3: 'SE', - 4: 'S', - 5: 'SW', - 6: 'W', - 7: 'NW' -} - - -def find_action_by_direction(direction): - for key, val in ACTION_LOOKUP.items(): - if val == direction: - return key - -register( - id='MazeF1-v0', - entry_point='gym_maze.envs:MazeF1', - max_episode_steps=50, - nondeterministic=False -) - -register( - id='MazeF2-v0', - entry_point='gym_maze.envs:MazeF2', - max_episode_steps=50, - nondeterministic=False -) - -register( - id='MazeF3-v0', - entry_point='gym_maze.envs:MazeF3', - max_episode_steps=50, - nondeterministic=False -) - -register( - id='MazeF4-v0', - entry_point='gym_maze.envs:MazeF4', - max_episode_steps=50, - nondeterministic=True -) - -register( - id='Maze4-v0', - entry_point='gym_maze.envs:Maze4', - max_episode_steps=50, - nondeterministic=False -) - -register( - id='Maze5-v0', - entry_point='gym_maze.envs:Maze5', - max_episode_steps=50, - nondeterministic=False -) - -register( - id='Maze6-v0', - entry_point='gym_maze.envs:Maze6', - max_episode_steps=50, - nondeterministic=True -) - -register( - id='BMaze4-v0', - entry_point='gym_maze.envs:BMaze4', - max_episode_steps=50, - nondeterministic=False -) - -register( - id='Woods1-v0', - entry_point='gym_maze.envs:Woods1', - max_episode_steps=50, - nondeterministic=False -) - -register( - id='Woods14-v0', - entry_point='gym_maze.envs:Woods14', - max_episode_steps=50, - nondeterministic=False -) \ No newline at end of file diff --git a/gym_maze/envs/BMaze4.py b/gym_maze/envs/BMaze4.py deleted file mode 100644 index 1c37b1d7..00000000 --- a/gym_maze/envs/BMaze4.py +++ /dev/null @@ -1,17 +0,0 @@ -from gym_maze.envs import AbstractMaze - -import numpy as np - - -class BMaze4(AbstractMaze): - def __init__(self): - super().__init__(np.matrix([ - [1, 1, 1, 1, 1, 1, 1, 1], - [1, 0, 0, 1, 0, 0, 9, 1], - [1, 1, 0, 0, 1, 0, 0, 1], - [1, 1, 0, 1, 0, 0, 1, 1], - [1, 0, 0, 0, 0, 0, 0, 1], - [1, 1, 0, 1, 0, 0, 0, 1], - [1, 0, 0, 0, 0, 1, 0, 1], - [1, 1, 1, 1, 1, 1, 1, 1], - ])) diff --git a/gym_maze/envs/Maze4.py b/gym_maze/envs/Maze4.py deleted file mode 100644 index c6112a27..00000000 --- a/gym_maze/envs/Maze4.py +++ /dev/null @@ -1,17 +0,0 @@ -from gym_maze.envs import AbstractMaze - -import numpy as np - - -class Maze4(AbstractMaze): - def __init__(self): - super().__init__(np.matrix([ - [1, 1, 1, 1, 1, 1, 1, 1], - [1, 0, 0, 1, 0, 0, 9, 1], - [1, 1, 0, 0, 1, 0, 0, 1], - [1, 1, 0, 1, 0, 0, 1, 1], - [1, 0, 0, 0, 0, 0, 0, 1], - [1, 1, 0, 1, 0, 0, 0, 1], - [1, 0, 0, 0, 0, 1, 0, 1], - [1, 1, 1, 1, 1, 1, 1, 1] - ])) diff --git a/gym_maze/envs/Maze5.py b/gym_maze/envs/Maze5.py deleted file mode 100644 index 5ed538fa..00000000 --- a/gym_maze/envs/Maze5.py +++ /dev/null @@ -1,18 +0,0 @@ -from gym_maze.envs import AbstractMaze - -import numpy as np - - -class Maze5(AbstractMaze): - def __init__(self): - super().__init__(np.matrix([ - [1, 1, 1, 1, 1, 1, 1, 1, 1], - [1, 0, 0, 0, 0, 0, 0, 9, 1], - [1, 0, 0, 1, 0, 1, 1, 0, 1], - [1, 0, 1, 0, 0, 0, 0, 0, 1], - [1, 0, 0, 0, 1, 1, 0, 0, 1], - [1, 0, 1, 0, 1, 0, 0, 1, 1], - [1, 0, 1, 0, 0, 1, 0, 0, 1], - [1, 0, 0, 0, 0, 0, 1, 0, 1], - [1, 1, 1, 1, 1, 1, 1, 1, 1], - ])) diff --git a/gym_maze/envs/Maze6.py b/gym_maze/envs/Maze6.py deleted file mode 100644 index b2241629..00000000 --- a/gym_maze/envs/Maze6.py +++ /dev/null @@ -1,18 +0,0 @@ -from gym_maze.envs import AbstractMaze - -import numpy as np - - -class Maze6(AbstractMaze): - def __init__(self): - super().__init__(np.matrix([ - [1, 1, 1, 1, 1, 1, 1, 1, 1], - [1, 0, 0, 0, 0, 0, 1, 9, 1], - [1, 0, 0, 1, 0, 1, 1, 0, 1], - [1, 0, 1, 0, 0, 0, 0, 0, 1], - [1, 0, 0, 0, 1, 1, 0, 0, 1], - [1, 0, 1, 0, 1, 0, 0, 1, 1], - [1, 0, 1, 0, 0, 1, 0, 0, 1], - [1, 0, 0, 0, 0, 0, 0, 0, 1], - [1, 1, 1, 1, 1, 1, 1, 1, 1], - ])) diff --git a/gym_maze/envs/MazeF1.py b/gym_maze/envs/MazeF1.py deleted file mode 100644 index 7bd3df10..00000000 --- a/gym_maze/envs/MazeF1.py +++ /dev/null @@ -1,15 +0,0 @@ -from gym_maze.envs import AbstractMaze - -import numpy as np - - -class MazeF1(AbstractMaze): - def __init__(self): - super().__init__(np.matrix([ - [1, 1, 1, 1], - [1, 0, 9, 1], - [1, 0, 1, 1], - [1, 0, 0, 1], - [1, 0, 1, 1], - [1, 1, 1, 1], - ])) diff --git a/gym_maze/envs/MazeF2.py b/gym_maze/envs/MazeF2.py deleted file mode 100644 index 1895dbcd..00000000 --- a/gym_maze/envs/MazeF2.py +++ /dev/null @@ -1,15 +0,0 @@ -from gym_maze.envs import AbstractMaze - -import numpy as np - - -class MazeF2(AbstractMaze): - def __init__(self): - super().__init__(np.matrix([ - [1, 1, 1, 1, 1], - [1, 0, 0, 9, 1], - [1, 0, 1, 1, 1], - [1, 0, 0, 1, 1], - [1, 0, 1, 1, 1], - [1, 1, 1, 1, 1], - ])) diff --git a/gym_maze/envs/MazeF3.py b/gym_maze/envs/MazeF3.py deleted file mode 100644 index 380a0ccd..00000000 --- a/gym_maze/envs/MazeF3.py +++ /dev/null @@ -1,15 +0,0 @@ -from gym_maze.envs import AbstractMaze - -import numpy as np - - -class MazeF3(AbstractMaze): - def __init__(self): - super().__init__(np.matrix([ - [1, 1, 1, 1, 1, 1], - [1, 0, 0, 0, 9, 1], - [1, 0, 1, 1, 1, 1], - [1, 0, 0, 0, 1, 1], - [1, 0, 1, 1, 1, 1], - [1, 1, 1, 1, 1, 1], - ])) \ No newline at end of file diff --git a/gym_maze/envs/MazeF4.py b/gym_maze/envs/MazeF4.py deleted file mode 100644 index 1267700e..00000000 --- a/gym_maze/envs/MazeF4.py +++ /dev/null @@ -1,15 +0,0 @@ -from gym_maze.envs import AbstractMaze - -import numpy as np - - -class MazeF4(AbstractMaze): - def __init__(self): - super().__init__(np.matrix([ - [1, 1, 1, 1, 1, 1, 1], - [1, 0, 0, 0, 0, 9, 1], - [1, 0, 1, 1, 1, 1, 1], - [1, 0, 0, 0, 0, 1, 1], - [1, 0, 1, 1, 1, 1, 1], - [1, 1, 1, 1, 1, 1, 1], - ])) diff --git a/gym_maze/envs/Woods1.py b/gym_maze/envs/Woods1.py deleted file mode 100644 index edc0b1e2..00000000 --- a/gym_maze/envs/Woods1.py +++ /dev/null @@ -1,14 +0,0 @@ -from gym_maze.envs import AbstractMaze - -import numpy as np - - -class Woods1(AbstractMaze): - def __init__(self): - super().__init__(np.matrix([ - [1, 1, 1, 1, 1], - [1, 0, 0, 9, 1], - [1, 0, 0, 0, 1], - [1, 0, 0, 0, 1], - [1, 1, 1, 1, 1], - ])) diff --git a/gym_maze/envs/Woods14.py b/gym_maze/envs/Woods14.py deleted file mode 100644 index fc960e2e..00000000 --- a/gym_maze/envs/Woods14.py +++ /dev/null @@ -1,16 +0,0 @@ -from gym_maze.envs import AbstractMaze - -import numpy as np - - -class Woods14(AbstractMaze): - def __init__(self): - super().__init__(np.matrix([ - [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], - [1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1], - [1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1], - [1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1], - [1, 9, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1], - [1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1], - [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], - ])) diff --git a/gym_maze/envs/__init__.py b/gym_maze/envs/__init__.py deleted file mode 100644 index b687d602..00000000 --- a/gym_maze/envs/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -from gym_maze.envs.abstract_maze import AbstractMaze -from gym_maze.envs.MazeF1 import MazeF1 -from gym_maze.envs.MazeF2 import MazeF2 -from gym_maze.envs.MazeF3 import MazeF3 -from gym_maze.envs.MazeF4 import MazeF4 -from gym_maze.envs.Maze4 import Maze4 -from gym_maze.envs.Maze5 import Maze5 -from gym_maze.envs.Maze6 import Maze6 -from gym_maze.envs.BMaze4 import BMaze4 -from gym_maze.envs.Woods1 import Woods1 -from gym_maze.envs.Woods14 import Woods14 diff --git a/gym_maze/envs/abstract_maze.py b/gym_maze/envs/abstract_maze.py deleted file mode 100644 index 668994f0..00000000 --- a/gym_maze/envs/abstract_maze.py +++ /dev/null @@ -1,145 +0,0 @@ -import logging -import random -import sys - -import gym -import numpy as np -from gym import spaces, utils - -from gym_maze import ACTION_LOOKUP -from gym_maze.maze import Maze, WALL_MAPPING -from gym_maze.utils import get_all_possible_transitions - -ANIMAT_MARKER = 5 - - -class AbstractMaze(gym.Env): - metadata = {'render.modes': ['human']} - - def __init__(self, matrix): - self.maze = Maze(matrix) - self.pos_x = None - self.pos_y = None - - self.action_space = spaces.Discrete(8) - self.observation_space = spaces.Discrete(8) - - def _step(self, action): - previous_observation = self._observe() - self._take_action(action, previous_observation) - - observation = self._observe() - reward = self._get_reward() - episode_over = self._is_over() - - return observation, reward, episode_over, {} - - def _reset(self): - logging.debug("Resetting the environment") - self._insert_animat() - return self._observe() - - def _render(self, mode='human', close=False): - if close: - return - - logging.debug("Rendering the environment") - if mode == 'human': - outfile = sys.stdout - outfile.write("\n") - - situation = np.copy(self.maze.matrix) - situation[self.pos_y, self.pos_x] = ANIMAT_MARKER - - for row in situation: - outfile.write(" ".join(self._render_element(el) for el in row)) - outfile.write("\n") - else: - super(AbstractMaze, self).render(mode=mode) - - def _observe(self): - return self.maze.perception(self.pos_x, self.pos_y) - - def _get_reward(self): - if self.maze.is_reward(self.pos_x, self.pos_y): - return 1000 - - return 0 - - def _is_over(self): - return self.maze.is_reward(self.pos_x, self.pos_y) - - def get_all_possible_transitions(self): - """ - Debugging only - - :return: - """ - return get_all_possible_transitions(self) - - def _take_action(self, action, observation): - """Executes the action inside the maze""" - animat_moved = False - action_type = ACTION_LOOKUP[action] - - if action_type == "N" and not self.is_wall(observation[0]): - self.pos_y -= 1 - animat_moved = True - - if action_type == 'NE' and not self.is_wall(observation[1]): - self.pos_x += 1 - self.pos_y -= 1 - animat_moved = True - - if action_type == "E" and not self.is_wall(observation[2]): - self.pos_x += 1 - animat_moved = True - - if action_type == 'SE' and not self.is_wall(observation[3]): - self.pos_x += 1 - self.pos_y += 1 - animat_moved = True - - if action_type == "S" and not self.is_wall(observation[4]): - self.pos_y += 1 - animat_moved = True - - if action_type == 'SW' and not self.is_wall(observation[5]): - self.pos_x -= 1 - self.pos_y += 1 - animat_moved = True - - if action_type == "W" and not self.is_wall(observation[6]): - self.pos_x -= 1 - animat_moved = True - - if action_type == 'NW' and not self.is_wall(observation[7]): - self.pos_x -= 1 - self.pos_y -= 1 - animat_moved = True - - return animat_moved - - def _insert_animat(self): - possible_coords = self.maze.get_possible_insertion_coordinates() - - starting_position = random.choice(possible_coords) - self.pos_x = starting_position[0] - self.pos_y = starting_position[1] - - @staticmethod - def is_wall(perception): - return perception == str(WALL_MAPPING) - - @staticmethod - def _render_element(el): - if el == 1: - return utils.colorize('■', 'gray') - elif el == 0: - return utils.colorize('□', 'white') - elif el == 9: - return utils.colorize('$', 'yellow') - elif el == ANIMAT_MARKER: - return utils.colorize('A', 'red') - else: - return utils.colorize(el, 'cyan') diff --git a/gym_maze/maze.py b/gym_maze/maze.py deleted file mode 100644 index bf593479..00000000 --- a/gym_maze/maze.py +++ /dev/null @@ -1,176 +0,0 @@ -PATH_MAPPING = 0 -WALL_MAPPING = 1 -REWARD_MAPPING = 9 - - -class Maze: - """ - Creates new maze. - - Mapping: - 0 - path - 1 - wall - 9 - reward - """ - - def __init__(self, matrix): - self.matrix = matrix - self.max_x = self.matrix.shape[1] - self.max_y = self.matrix.shape[0] - - def get_possible_insertion_coordinates(self): - """ - Returns a list with coordinates in the environment where - an agent can be placed (only on the path). - :return: list of tuples (X,Y) containing coordinates - """ - possible_cords = [] - for x in range(0, self.max_x): - for y in range(0, self.max_y): - if self.is_path(x, y): - possible_cords.append((x, y)) - - return possible_cords - - def perception(self, pos_x, pos_y): - if not self._within_x_range(pos_x): - raise ValueError('X position not within allowed range') - - if not self._within_y_range(pos_y): - raise ValueError('Y position not within allowed range') - - # Position N - if pos_y == 0: - n = None - else: - n = str(self.matrix[pos_y - 1, pos_x]) - - # Position NE - if pos_x == self.max_x - 1 or pos_y == 0: - ne = None - else: - ne = str(self.matrix[pos_y - 1, pos_x + 1]) - - # Position E - if pos_x == self.max_x - 1: - e = None - else: - e = str(self.matrix[pos_y, pos_x + 1]) - - # Position SE - if pos_x == self.max_x - 1 or pos_y == self.max_y - 1: - se = None - else: - se = str(self.matrix[pos_y + 1, pos_x + 1]) - - # Position S - if pos_y == (self.max_y - 1): - s = None - else: - s = str(self.matrix[pos_y + 1, pos_x]) - - # Position SW - if pos_x == 0 or pos_y == self.max_y - 1: - sw = None - else: - sw = str(self.matrix[pos_y + 1, pos_x - 1]) - - # Position W - if pos_x == 0: - w = None - else: - w = str(self.matrix[pos_y, pos_x - 1]) - - # Position NW - if pos_x == 0 or pos_y == 0: - nw = None - else: - nw = str(self.matrix[pos_y - 1, pos_x - 1]) - - return n, ne, e, se, s, sw, w, nw - - def is_wall(self, pos_x, pos_y): - return self.matrix[pos_y, pos_x] == WALL_MAPPING - - def is_path(self, pos_x, pos_y): - return self.matrix[pos_y, pos_x] == PATH_MAPPING - - def is_reward(self, pos_x, pos_y): - return self.matrix[pos_y, pos_x] == REWARD_MAPPING - - def _within_x_range(self, x): - return 0 <= x < self.max_x - - def _within_y_range(self, y): - return 0 <= y < self.max_y - - @staticmethod - def moved_north(start, destination) -> bool: - """ - :param start: start (X, Y) coordinates tuple - :param destination: destination (X, Y) coordinates tuple - :return: true if it was north move - """ - return destination[1] + 1 == start[1] - - @staticmethod - def moved_east(start, destination) -> bool: - """ - :param start: start (X, Y) coordinates tuple - :param destination: destination (X, Y) coordinates tuple - :return: true if it was east move - """ - return destination[0] - 1 == start[0] - - @staticmethod - def moved_south(start, destination) -> bool: - """ - :param start: start (X, Y) coordinates tuple - :param destination: destination (X, Y) coordinates tuple - :return: true if it was south move - """ - return destination[1] - 1 == start[1] - - @staticmethod - def moved_west(start, destination) -> bool: - """ - :param start: start (X, Y) coordinates tuple - :param destination: destination (X, Y) coordinates tuple - :return: true if it was west move - """ - return destination[0] + 1 == start[0] - - @staticmethod - def distinguish_direction(start, end): - direction = '' - - if Maze.moved_north(start, end): - direction += 'N' - - if Maze.moved_south(start, end): - direction += 'S' - - if Maze.moved_west(start, end): - direction += 'W' - - if Maze.moved_east(start, end): - direction += 'E' - - return direction - - @staticmethod - def get_possible_neighbour_cords(pos_x, pos_y) -> tuple: - """ - Returns a tuple with coordinates for - N, NE, E, SE, S, SW, W, NW neighbouring cells. - """ - n = (pos_x, pos_y - 1) - ne = (pos_x + 1, pos_y - 1) - e = (pos_x + 1, pos_y) - se = (pos_x + 1, pos_y + 1) - s = (pos_x, pos_y + 1) - sw = (pos_x - 1, pos_y + 1) - w = (pos_x - 1, pos_y) - nw = (pos_x - 1, pos_y - 1) - - return n, ne, e, se, s, sw, w, nw diff --git a/gym_maze/tests/__init__.py b/gym_maze/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/gym_maze/tests/test_utils.py b/gym_maze/tests/test_utils.py deleted file mode 100644 index 7031072d..00000000 --- a/gym_maze/tests/test_utils.py +++ /dev/null @@ -1,15 +0,0 @@ -import gym -# noinspection PyUnresolvedReferences -import gym_maze - - -class TestUtils: - def test_should_calculate_transitions(self): - # given - maze = gym.make("Woods1-v0") - - # when - transitions = maze.env.get_all_possible_transitions() - - # then - assert 37 == len(transitions) diff --git a/gym_maze/utils/__init__.py b/gym_maze/utils/__init__.py deleted file mode 100644 index 5cf88c30..00000000 --- a/gym_maze/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .utils import get_all_possible_transitions diff --git a/gym_maze/utils/utils.py b/gym_maze/utils/utils.py deleted file mode 100644 index 28c758af..00000000 --- a/gym_maze/utils/utils.py +++ /dev/null @@ -1,58 +0,0 @@ -import networkx as nx - -from gym_maze import find_action_by_direction -from gym_maze.maze import Maze - - -def get_all_possible_transitions(maze): - """ - Returns all possible transitions within the maze. - [POINT]->[ACTION]->[POINT] - This information is used to calculate the agent's knowledge - :param maze: an instance of the maze - :return: - """ - transitions = [] - - g = _create_graph(maze) - - path_nodes = (node for node, data - in g.nodes(data=True) if data['type'] == 'path') - - for node in path_nodes: - for neighbour in nx.all_neighbors(g, node): - direction = Maze.distinguish_direction(node, neighbour) - action = find_action_by_direction(direction) - - transitions.append((node, action, neighbour)) - - return transitions - - -def _create_graph(env): - maze = env.maze - - # Create uni-directed graph - g = nx.Graph() - - # Add nodes - for x in range(0, maze.max_x): - for y in range(0, maze.max_y): - if maze.is_path(x, y): - g.add_node((x, y), type='path') - if maze.is_reward(x, y): - g.add_node((x, y), type='reward') - - # Add edges - path_nodes = [cords for cords, attribs - in g.nodes(data=True) if attribs['type'] == 'path'] - - for n in path_nodes: - neighbour_cells = Maze.get_possible_neighbour_cords(*n) - allowed_cells = [c for c in neighbour_cells - if maze.is_path(*c) or maze.is_reward(*c)] - edges = [(n, dest) for dest in allowed_cells] - - g.add_edges_from(edges) - - return g \ No newline at end of file diff --git a/gym_multiplexer/__init__.py b/gym_multiplexer/__init__.py deleted file mode 100644 index ebab56ad..00000000 --- a/gym_multiplexer/__init__.py +++ /dev/null @@ -1,60 +0,0 @@ -from gym.envs.registration import register - -from .boolean_multiplexer import BooleanMultiplexer -from .real_multiplexer import RealMultiplexer - -bool_mpx_name = "boolean-multiplexer" -real_mpx_name = "real-multiplexer" -max_episode_steps = 1 - -# Length of a multiplexer is calculated -# using l = k + 2^k - -register( - id='{}-3bit-v0'.format(bool_mpx_name), - entry_point='gym_multiplexer:BooleanMultiplexer', - max_episode_steps=max_episode_steps, - kwargs={'control_bits': 1} -) - -register( - id='{}-6bit-v0'.format(bool_mpx_name), - entry_point='gym_multiplexer:BooleanMultiplexer', - max_episode_steps=max_episode_steps, - kwargs={'control_bits': 2} -) - -register( - id='{}-11bit-v0'.format(bool_mpx_name), - entry_point='gym_multiplexer:BooleanMultiplexer', - max_episode_steps=max_episode_steps, - kwargs={'control_bits': 3} -) - -register( - id='{}-20bit-v0'.format(bool_mpx_name), - entry_point='gym_multiplexer:BooleanMultiplexer', - max_episode_steps=max_episode_steps, - kwargs={'control_bits': 4} -) - -register( - id='{}-37bit-v0'.format(bool_mpx_name), - entry_point='gym_multiplexer:BooleanMultiplexer', - max_episode_steps=max_episode_steps, - kwargs={'control_bits': 5} -) - -register( - id='{}-3bit-v0'.format(real_mpx_name), - entry_point='gym_multiplexer:RealMultiplexer', - max_episode_steps=max_episode_steps, - kwargs={'control_bits': 1} -) - -register( - id='{}-6bit-v0'.format(real_mpx_name), - entry_point='gym_multiplexer:RealMultiplexer', - max_episode_steps=max_episode_steps, - kwargs={'control_bits': 2} -) \ No newline at end of file diff --git a/gym_multiplexer/boolean_multiplexer.py b/gym_multiplexer/boolean_multiplexer.py deleted file mode 100644 index fecf09ec..00000000 --- a/gym_multiplexer/boolean_multiplexer.py +++ /dev/null @@ -1,19 +0,0 @@ -import random - -from gym.spaces import Discrete - -from .multiplexer import Multiplexer - - -class BooleanMultiplexer(Multiplexer): - - def __init__(self, control_bits=3) -> None: - super().__init__(control_bits) - self.observation_space = Discrete(self._observation_string_length) - self.action_space = Discrete(2) - - def _generate_state(self): - return [random.randint(0, 1) for _ in range(0, self._observation_string_length - 1)] - - def _internal_state(self): - return list(map(lambda x: round(x), self._state)) diff --git a/gym_multiplexer/multiplexer.py b/gym_multiplexer/multiplexer.py deleted file mode 100644 index 06b4ddce..00000000 --- a/gym_multiplexer/multiplexer.py +++ /dev/null @@ -1,57 +0,0 @@ -import random - -import gym - -from .utils import get_correct_answer - - -class Multiplexer(gym.Env): - - REWARD = 1000 - - def _generate_state(self): raise NotImplementedError - def _internal_state(self): raise NotImplementedError - - def __init__(self, control_bits=3) -> None: - self.control_bits = control_bits - self.metadata = {'render.modes': ['human']} - - self._state = None - self._validation_bit = 0 - - def _reset(self): - self._state = self._generate_state() - self._validation_bit = 0 - return self._observation - - def _step(self, action): - reward = 0 - - if action == self._correct_answer: - self._validation_bit = 1 - reward = self.REWARD - - return self._observation, reward, None, None - - def _render(self, mode='human', close=False): - if close: - return - - if mode == 'human': - return self._observation - - return self.render(mode=mode) - - @property - def _observation(self) -> list: - observation = list(self._state) - observation.append(self._validation_bit) - return observation - - @property - def _correct_answer(self): - return get_correct_answer(list(self._internal_state()) , self.control_bits) - - @property - def _observation_string_length(self): - return self.control_bits + pow(2, self.control_bits) + 1 \ No newline at end of file diff --git a/gym_multiplexer/real_multiplexer.py b/gym_multiplexer/real_multiplexer.py deleted file mode 100644 index 08e1d38c..00000000 --- a/gym_multiplexer/real_multiplexer.py +++ /dev/null @@ -1,20 +0,0 @@ -import random - -from gym.spaces import Box, Discrete - -from .multiplexer import Multiplexer - - -class RealMultiplexer(Multiplexer): - - def __init__(self, control_bits=3, threshold=.5) -> None: - super().__init__(control_bits) - self.threshold = threshold - self.observation_space = Box(low=0, high=1, shape=(self._observation_string_length, )) - self.action_space = Discrete(2) - - def _generate_state(self): - return [random.random() for _ in range(0, self._observation_string_length - 1)] - - def _internal_state(self): - return list(map(lambda x: x > self.threshold, self._state)) diff --git a/gym_multiplexer/tests/__init__.py b/gym_multiplexer/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/gym_multiplexer/tests/test_boolean_multiplexer.py b/gym_multiplexer/tests/test_boolean_multiplexer.py deleted file mode 100644 index c50c4f36..00000000 --- a/gym_multiplexer/tests/test_boolean_multiplexer.py +++ /dev/null @@ -1,85 +0,0 @@ -import logging -import random -import sys - -import gym - -# noinspection PyUnresolvedReferences -import gym_multiplexer - -logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) - - -class TestBooleanMultiplexer: - def test_should_initialize_multiplexer(self): - # when - mp = gym.make('boolean-multiplexer-6bit-v0') - - # then - assert mp is not None - assert 7 == mp.observation_space.n - assert 2 == mp.action_space.n - - def test_should_return_observation_when_reset(self): - # given - mp = gym.make('boolean-multiplexer-6bit-v0') - - # when - state = mp.reset() - - # then - assert state is not None - assert state[-1] == 0 - assert 7 == len(state) - assert type(state) is list - for obs in state: - assert obs in [0, 1] - - def test_should_render_state(self): - # given - mp = gym.make('boolean-multiplexer-3bit-v0') - mp.reset() - - # when - state = mp.render() - - # then - assert state is not None - assert state[-1] == 0 - assert 4 == len(state) - assert type(state) is list - - def test_should_execute_step(self): - # given - mp = gym.make('boolean-multiplexer-3bit-v0') - mp.reset() - action = self._random_action() - - # when - state, reward, done, _ = mp.step(action) - - # then - assert state is not None - assert type(state) is list - assert reward in [0, 1000] - assert done is True - for obs in state: - assert obs in [0, 1] - - def test_execute_multiple_steps_and_keep_constant_perception_length(self): - # given - mp = gym.make('boolean-multiplexer-6bit-v0') - steps = 100 - - # when & then - for _ in range(0, steps): - p0 = mp.reset() - assert 7 == len(p0) - - action = self._random_action() - p1, reward, done, _ = mp.step(action) - assert 7 == len(p1) - - @staticmethod - def _random_action(): - return random.sample([0, 1], 1)[0] diff --git a/gym_multiplexer/tests/test_real_multiplexer.py b/gym_multiplexer/tests/test_real_multiplexer.py deleted file mode 100644 index b6ab8517..00000000 --- a/gym_multiplexer/tests/test_real_multiplexer.py +++ /dev/null @@ -1,68 +0,0 @@ -import logging -import random -import sys - -import gym - -# noinspection PyUnresolvedReferences -import gym_multiplexer - -logging.basicConfig(level=logging.DEBUG, stream=sys.stdout) - -class TestRealMultiplexer: - - def test_should_initialize_real_mpx(self): - # when - mp = gym.make("real-multiplexer-6bit-v0") - - # then - assert mp is not None - assert (7,) == mp.observation_space.shape - assert 2 == mp.action_space.n - - def test_should_return_observation_when_reset(self): - # given - mp = gym.make('real-multiplexer-6bit-v0') - - # when - state = mp.reset() - - # then - assert state is not None - assert state[-1] == 0 - assert 7 == len(state) - assert type(state) is list - - def test_should_execute_step(self): - # given - mp = gym.make('real-multiplexer-6bit-v0') - mp.reset() - action = self._random_action() - - # when - state, reward, done, _ = mp.step(action) - - # then - assert state is not None - assert state[-1] in [0, 1] - assert type(state) is list - assert reward in [0, 1000] - assert done is True - - def test_execute_multiple_steps_and_keep_constant_perception_length(self): - # given - mp = gym.make('real-multiplexer-6bit-v0') - steps = 100 - - # when & then - for _ in range(0, steps): - p0 = mp.reset() - assert 7 == len(p0) - - action = self._random_action() - p1, reward, done, _ = mp.step(action) - assert 7 == len(p1) - - @staticmethod - def _random_action(): - return random.sample([0, 1], 1)[0] \ No newline at end of file diff --git a/gym_multiplexer/tests/test_utils.py b/gym_multiplexer/tests/test_utils.py deleted file mode 100644 index f000de16..00000000 --- a/gym_multiplexer/tests/test_utils.py +++ /dev/null @@ -1,14 +0,0 @@ -from gym_multiplexer.utils import get_correct_answer - - -class TestUtils: - - def test_should_calculate_correct_answer_for_3bit_multiplexer(self): - assert 1 == get_correct_answer([0,1,0,0], 1) - assert 0 == get_correct_answer([1,1,0,0], 1) - - def test_should_calculate_correct_answer_for_6bit_multiplexer(self): - assert 0 == get_correct_answer([1,1,0,1,0,0,0], 2) - - def test_should_calculate_correct_answer_for_11bit_multiplexer(self): - assert 1 == get_correct_answer([1,0,1,1,0,1,1,0,1,0], 3) diff --git a/gym_multiplexer/utils/__init__.py b/gym_multiplexer/utils/__init__.py deleted file mode 100644 index cd0c6656..00000000 --- a/gym_multiplexer/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .utils import get_correct_answer \ No newline at end of file diff --git a/gym_multiplexer/utils/utils.py b/gym_multiplexer/utils/utils.py deleted file mode 100644 index a243fe4f..00000000 --- a/gym_multiplexer/utils/utils.py +++ /dev/null @@ -1,10 +0,0 @@ -from bitstring import BitString - - -def get_correct_answer(bitstring: list, control_bits: int) -> int: - bits = BitString(bitstring) - - _ctrl_bits = bits[:control_bits] - _data_bits = bits[control_bits:] - - return int(_data_bits[_ctrl_bits.uint]) diff --git a/requirements.txt b/requirements.txt index 500a1c3b..71b1d726 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,10 @@ --e . - +twine +pep8==1.7.0 +coverage==4.3.4 +jupyter==1.0.0 +matplotlib==2.1.0 +gym[all]==0.9.4 +pandas==0.21.0 pytest==3.2.3 + +-e git+https://github.com/ParrotPrediction/openai-envs.git#egg=parrotprediction-openai-envs diff --git a/setup.py b/setup.py index d89547bb..3113a026 100644 --- a/setup.py +++ b/setup.py @@ -1,18 +1,29 @@ from setuptools import setup, find_packages -setup(name='parrotprediction-openai-envs', - version='2.0.3', - description='Custom environments for OpenAI Gym', +setup(name='pyalcs', + version='1.4', + description='Implementation of Anticipatory Learning Classifiers', keywords='acs lcs machine-learning reinforcement-learning openai', - url='https://github.com/ParrotPrediction/openai-envs', - author='Parrot Prediction Ltd.', - author_email='contact@parrotprediction.com', + url='https://github.com/ParrotPrediction/pyalcs', + author='Parrot Prediction Ltd', + author_email='norbert@parrotprediction.com', license='MIT', + classifiers=[ + 'Development Status :: 4 - Beta', + 'Intended Audience :: Science/Research', + 'Intended Audience :: Information Technology', + 'License :: OSI Approved :: MIT License', + 'Programming Language :: Python :: 3.6', + 'Topic :: Scientific/Engineering :: Artificial Intelligence' + ], + project_urls={ + 'Source': 'https://github.com/ParrotPrediction/pyalcs', + 'Tracking': 'https://github.com/ParrotPrediction/pyalcs/issues', + }, + python_requires='>=3.5', packages=find_packages(), install_requires=[ - 'gym>=0.10', - 'networkx==2.0', - 'bitstring==3.1.5' + ], include_package_data=False, # We don't have other types of files -zip_safe=False) + zip_safe=False) From 364bcd1dd5f12ac5b9d241ab892170210ce92147 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Fri, 31 Aug 2018 09:54:33 +0200 Subject: [PATCH 35/83] action planning in progress --- alcs/acs2/ACS2.py | 45 +++++++++++++++++++++----- alcs/acs2/ACS2Configuration.py | 8 +++++ alcs/acs2/ClassifiersList.py | 4 +++ integration/handeye/acs2_in_handeye.py | 1 + 4 files changed, 50 insertions(+), 8 deletions(-) diff --git a/alcs/acs2/ACS2.py b/alcs/acs2/ACS2.py index 62ed818b..bb3b7550 100644 --- a/alcs/acs2/ACS2.py +++ b/alcs/acs2/ACS2.py @@ -81,6 +81,10 @@ def _run_trial_explore(self, env, time, current_trial=None): done = False while not done: + if self.cfg.do_action_planning and (steps + time) % self.cfg.action_planning_frequency == 0: + # TODO: check if HandEye? + self._run_action_planning(env, steps + time, state, prev_state, action_set, action, reward) + match_set = ClassifiersList.form_match_set(self.population, state, self.cfg) @@ -174,22 +178,47 @@ def _run_trial_exploit(self, env, time=None, current_trial=None): return steps def _run_action_planning(self, env, time, situation, - previous_situation, actionSet, act, rho0): + previous_situation, action_set, action, reward): + + if not hasattr(env, "get_goal_state"): + return 0 + steps = 0 - matchSet = 0 # TODO ClassifierList *matchSet = 0; + match_set = 0 done = False - goal_situation = 0 # TODO env->getGoalState(goalSituation) + goal_situation = 0 while not done: - # TODO if (!env->getGoalState(goalSituation)) {break; - act_sequence = self.population.search_goal_sequence(situation, goal_situation) + goal_situation = env.get_goal_state() + + if goal_situation is None: + return 0 + + act_sequence = self.population.search_goal_sequence(situation, goal_situation) # TODO: search_goal_sequence (ClassifierList) i = 0 while act_sequence[i] != 0: - matchSet = ClassifiersList() #TODO new constructor??? + match_set = ClassifiersList(self.population, situation) # TODO new constructor??? + if action_set is not None: + action_set.apply_alp(previous_situation, action, situation, time + steps, self.population, match_set) + action_set.apply_reinforcement_learning(reward, match_set.get_maximum_fitness()) + if self.cfg.do_ga: + action_set.apply_ga(time + steps, self.population, match_set, situation) + + action = act_sequence[i] + action_set = ClassifiersList(match_set, action) + + previous_situation = situation + raw_state, reward, done, _ = env.step(self._parse_action(action)) + situation = self._parse_state(raw_state) + + if action_set.exists_classifier(previous_situation, action, situation, self.cfg.theta_r): + break i += 1 - pass - pass + steps += 1 + + if not(i == 0 or action != 0): # TODO: necessary? + break return steps diff --git a/alcs/acs2/ACS2Configuration.py b/alcs/acs2/ACS2Configuration.py index 7bbeee25..284bda90 100644 --- a/alcs/acs2/ACS2Configuration.py +++ b/alcs/acs2/ACS2Configuration.py @@ -11,6 +11,8 @@ def __init__(self, performance_fcn_params={}, do_ga=False, do_subsumption=True, + do_action_planning=False, + action_planning_frequency=50, beta=0.05, gamma=0.95, theta_i=0.1, @@ -39,6 +41,8 @@ def __init__(self, calculating agent performance :param do_ga: switch *Genetic Generalization* module :param do_subsumption: + :param do_action_planning: switch Action Planning phase + :param action_planning_frequency: :param beta: :param gamma: :param theta_i: @@ -63,6 +67,8 @@ def __init__(self, self.performance_fcn_params = performance_fcn_params self.do_ga = do_ga self.do_subsumption = do_subsumption + self.do_action_planning = do_action_planning + self.action_planning_frequency = action_planning_frequency self.theta_exp = theta_exp self.beta = beta self.gamma = gamma @@ -86,6 +92,7 @@ def __str__(self): "\n\t- Performance calculation function: [{}] " \ "\n\t- Do GA: [{}]" \ "\n\t- Do subsumption: [{}]" \ + "\n\t- Do Action Planning: [{}]" \ "\n\t- Beta: [{}]" \ "\n\t- ..." \ "\n\t- Epsilon: [{}]" \ @@ -99,6 +106,7 @@ def __str__(self): self.performance_fcn, self.do_ga, self.do_subsumption, + self.do_action_planning, self.beta, self.epsilon, self.u_max) diff --git a/alcs/acs2/ClassifiersList.py b/alcs/acs2/ClassifiersList.py index c54df3d1..7caa24cb 100644 --- a/alcs/acs2/ClassifiersList.py +++ b/alcs/acs2/ClassifiersList.py @@ -533,3 +533,7 @@ def find_subsumer(self, cl: Classifier, choice_func=choice) -> Classifier: def search_goal_sequence(self, start, goal): #TODO: Action **ClassifierList::searchGoalSequence(Perception *start, Perception *goal) { pass + + def exists_classifier(self, previous_situation, action, situation, quality): + #TODO: state = self._parse_state(raw_state) + pass diff --git a/integration/handeye/acs2_in_handeye.py b/integration/handeye/acs2_in_handeye.py index 5178e01e..39a29bea 100644 --- a/integration/handeye/acs2_in_handeye.py +++ b/integration/handeye/acs2_in_handeye.py @@ -23,6 +23,7 @@ cfg = ACS2Configuration(10, 6, epsilon=1.0, do_ga=False, + do_action_planning=True, performance_fcn=calculate_performance) logging.info(cfg) From c335d1e2d7e8b903a38153c1f6b8a1f4d6d4eb41 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Fri, 31 Aug 2018 10:58:34 +0200 Subject: [PATCH 36/83] implemented exists_classifier in ClassifiersList --- alcs/acs2/ACS2.py | 6 ++++-- alcs/acs2/Classifier.py | 20 +++++++++++++------- alcs/acs2/ClassifiersList.py | 9 +++++++-- 3 files changed, 24 insertions(+), 11 deletions(-) diff --git a/alcs/acs2/ACS2.py b/alcs/acs2/ACS2.py index bb3b7550..44c41f36 100644 --- a/alcs/acs2/ACS2.py +++ b/alcs/acs2/ACS2.py @@ -197,7 +197,9 @@ def _run_action_planning(self, env, time, situation, act_sequence = self.population.search_goal_sequence(situation, goal_situation) # TODO: search_goal_sequence (ClassifierList) i = 0 while act_sequence[i] != 0: - match_set = ClassifiersList(self.population, situation) # TODO new constructor??? + match_set = ClassifiersList.form_match_set(self.population, + situation, + self.cfg) if action_set is not None: action_set.apply_alp(previous_situation, action, situation, time + steps, self.population, match_set) action_set.apply_reinforcement_learning(reward, match_set.get_maximum_fitness()) @@ -205,7 +207,7 @@ def _run_action_planning(self, env, time, situation, action_set.apply_ga(time + steps, self.population, match_set, situation) action = act_sequence[i] - action_set = ClassifiersList(match_set, action) + action_set = ClassifiersList.form_action_set(match_set, action, self.cfg) previous_situation = situation raw_state, reward, done, _ = env.step(self._parse_action(action)) diff --git a/alcs/acs2/Classifier.py b/alcs/acs2/Classifier.py index 72384adc..537e6350 100644 --- a/alcs/acs2/Classifier.py +++ b/alcs/acs2/Classifier.py @@ -142,7 +142,7 @@ def specified_unchanging_attributes(self): for cpi, epi in zip(self.condition, self.effect): if cpi != self.cfg.classifier_wildcard and \ - epi == self.cfg.classifier_wildcard: + epi == self.cfg.classifier_wildcard: spec += 1 return spec @@ -339,7 +339,7 @@ def mutate(self, randomfunc=random): """ for idx, cond in enumerate(self.condition): if cond != self.cfg.classifier_wildcard and \ - randomfunc() < self.cfg.mu: + randomfunc() < self.cfg.mu: self.condition.generalize(idx) def is_similar(self, other) -> bool: @@ -351,8 +351,8 @@ def is_similar(self, other) -> bool: :return: True if equals, False otherwise """ if self.condition == other.condition and \ - self.action == other.action and \ - self.effect == other.effect: + self.action == other.action and \ + self.effect == other.effect: return True return False @@ -369,7 +369,7 @@ def is_more_general(self, other) -> bool: return False def generalize_unchanging_condition_attribute( - self, no_spec, randomfunc=randint) -> bool: + self, no_spec, randomfunc=randint) -> bool: """ Generalizes one randomly unchanging attribute in the condition. An unchanging attribute is one that is anticipated not to change @@ -387,7 +387,7 @@ def generalize_unchanging_condition_attribute( for idx, (cpi, epi) in enumerate(zip(self.condition, self.effect)): if cpi != self.cfg.classifier_wildcard and \ - epi == self.cfg.classifier_wildcard: + epi == self.cfg.classifier_wildcard: if att_idx == pos: self.condition.generalize(idx) @@ -407,7 +407,7 @@ def does_subsume(self, other) -> bool: if self._is_subsumer() and \ self.is_more_general(other) and \ self.condition.does_match(other.condition) and \ - self.effect == other.effect: + self.effect == other.effect: return True return False @@ -452,3 +452,9 @@ def crossover(self, cl2, samplefunc=sample): cl2.q = q cl2.r = r + + def does_match(self, situation): + return self.condition.does_match(situation.condition) # TODO: check if correct + + def has_action(self, action): + return action == self.action diff --git a/alcs/acs2/ClassifiersList.py b/alcs/acs2/ClassifiersList.py index 7caa24cb..4cf684fe 100644 --- a/alcs/acs2/ClassifiersList.py +++ b/alcs/acs2/ClassifiersList.py @@ -535,5 +535,10 @@ def search_goal_sequence(self, start, goal): pass def exists_classifier(self, previous_situation, action, situation, quality): - #TODO: state = self._parse_state(raw_state) - pass + for cl in self: + if cl.q > quality: + if cl.does_match(previous_situation): + if cl.has_action(action): + if cl.does_anticipate_correctly(previous_situation, situation): + return 1 + return 0 From 2ba36cfe918ef304d0e4420b32b3bc8ecc113d68 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Tue, 18 Sep 2018 13:12:23 +0200 Subject: [PATCH 37/83] merged with action_planning branch --- alcs/acs2/ACS2.py | 61 ++-- alcs/acs2/Classifier.py | 51 +++- alcs/acs2/ClassifiersList.py | 280 ++++++++++++++++-- alcs/acs2/Condition.py | 13 + alcs/acs2/Effect.py | 49 +++ assets/ACS2/ACSConstants.h | 6 +- assets/ACS2/ClassifierList.cc | 1 + assets/ACS2/Effect.cc | 3 + assets/ACS2/HandEyeEnvironment.h | 2 +- assets/ACS2/acs2++.cc | 12 +- integration/handeye/acs2_in_handeye_ap.py | 39 +++ ...in_handeye.py => acs2_in_handeye_no_ap.py} | 4 +- tests/acs2/test_Classifier.py | 57 ++++ tests/acs2/test_ClassifierList.py | 115 ++++++- tests/acs2/test_Condition.py | 22 ++ tests/acs2/test_Effect.py | 98 ++++++ 16 files changed, 745 insertions(+), 68 deletions(-) create mode 100644 integration/handeye/acs2_in_handeye_ap.py rename integration/handeye/{acs2_in_handeye.py => acs2_in_handeye_no_ap.py} (86%) diff --git a/alcs/acs2/ACS2.py b/alcs/acs2/ACS2.py index 44c41f36..8da3e024 100644 --- a/alcs/acs2/ACS2.py +++ b/alcs/acs2/ACS2.py @@ -82,8 +82,9 @@ def _run_trial_explore(self, env, time, current_trial=None): while not done: if self.cfg.do_action_planning and (steps + time) % self.cfg.action_planning_frequency == 0: - # TODO: check if HandEye? - self._run_action_planning(env, steps + time, state, prev_state, action_set, action, reward) + # Action Planning for increased model learning + steps_ap, state, prev_state, action_set, reward = self._run_action_planning(env, steps + time, state, prev_state, action_set, action, reward) + steps += steps_ap match_set = ClassifiersList.form_match_set(self.population, state, @@ -179,50 +180,70 @@ def _run_trial_exploit(self, env, time=None, current_trial=None): def _run_action_planning(self, env, time, situation, previous_situation, action_set, action, reward): + """ + Executes action planning for model learning speed up. + Method requests goals from 'goal generator' provided by the environment. + If goal is provided, ACS2 searches for a goal sequence in the current model (only the reliable classifiers). + This is done as long as goals are provided and ACS2 finds a sequence and successfully reaches the goal. + :param env: + :param time: + :param situation: + :param previous_situation: + :param action_set: + :param action: + :param reward: + :return: + """ + logging.debug("** Running action planning **") - if not hasattr(env, "get_goal_state"): - return 0 + # The environment has to have a function "get_goal_state" + if not hasattr(env.env, "get_goal_state"): + logging.debug("Action planning stopped - no function get_goal_state in env") + return 0, situation, previous_situation, action_set, reward steps = 0 - match_set = 0 done = False - goal_situation = 0 while not done: - goal_situation = env.get_goal_state() + goal_situation = env.env.get_goal_state() if goal_situation is None: - return 0 + break - act_sequence = self.population.search_goal_sequence(situation, goal_situation) # TODO: search_goal_sequence (ClassifierList) + act_sequence = self.population.search_goal_sequence(situation, goal_situation) + + # Execute the found sequence and learn during executing i = 0 - while act_sequence[i] != 0: - match_set = ClassifiersList.form_match_set(self.population, - situation, - self.cfg) - if action_set is not None: + for act in act_sequence: + if act == -1: + break + + match_set = ClassifiersList.form_match_set(population=self.population, + situation=situation, cfg=self.cfg) + if action_set is not None and previous_situation is not None: action_set.apply_alp(previous_situation, action, situation, time + steps, self.population, match_set) action_set.apply_reinforcement_learning(reward, match_set.get_maximum_fitness()) if self.cfg.do_ga: action_set.apply_ga(time + steps, self.population, match_set, situation) - action = act_sequence[i] + action = act action_set = ClassifiersList.form_action_set(match_set, action, self.cfg) - previous_situation = situation raw_state, reward, done, _ = env.step(self._parse_action(action)) + previous_situation = situation situation = self._parse_state(raw_state) - if action_set.exists_classifier(previous_situation, action, situation, self.cfg.theta_r): + if not action_set.exists_classifier(previous_situation, action, situation, self.cfg.theta_r): + # no reliable classifier was able to anticipate such a change break - i += 1 steps += 1 + i += 1 - if not(i == 0 or action != 0): # TODO: necessary? + if i == 0: break - return steps + return steps, situation, previous_situation, action_set, reward def _parse_state(self, raw_state): """ diff --git a/alcs/acs2/Classifier.py b/alcs/acs2/Classifier.py index 537e6350..3f45e424 100644 --- a/alcs/acs2/Classifier.py +++ b/alcs/acs2/Classifier.py @@ -225,6 +225,12 @@ def predicts_successfully(self, def does_anticipate_correctly(self, previous_situation: Perception, situation: Perception) -> bool: + """ + Returns if the classifier correctly anticipates the change from previous_situation to situation. + :param previous_situation: + :param situation: + :return: + """ return self.effect.does_anticipate_correctly( previous_situation, situation) @@ -454,7 +460,46 @@ def crossover(self, cl2, samplefunc=sample): cl2.r = r def does_match(self, situation): - return self.condition.does_match(situation.condition) # TODO: check if correct + """ + Returns if the classifier matches the situation. + :param situation: + :return: + """ + return self.condition.does_match(situation) + + def does_match_backwards(self, situation): + """ + Returns if 'situation' is matched by the anticipations. + This is only the case if the specified conditions that have #-symbols in the effect part are also matched! + :param situation: + :return: + """ + p = self.condition.get_backwards_anticipation(situation) + if self.effect.does_match(situation, p): + return True + return False + + def get_best_anticipation(self, perception): + """ + Returns the anticipation, the classifier believes to happen most probably. + This is usually the normal anticipation. However, if PEEs are activated, the most probable + value of each attribute is returned. + :param perception: Perception + :return: + """ + return self.effect.get_best_anticipation(perception) - def has_action(self, action): - return action == self.action + def get_backwards_anticipation(self, perception): + """ + Returns the backwards anticipation. + Returns -1 if the backwards anticipation was impossible to create. This is the case if + changing attributes are not specified in the conditions. + :param perception: + :return: + """ + back_anticipation = self.condition.get_backwards_anticipation(perception) + if not self.effect.does_specify_only_changes_backwards(back_anticipation, perception): + # If a specified attribute in the effect part matches the anticipated 'back_anticipation', + # the backward anticipation fails, because a specified attribute in Effect means a change! + return None + return back_anticipation diff --git a/alcs/acs2/ClassifiersList.py b/alcs/acs2/ClassifiersList.py index 4cf684fe..0cfcbefc 100644 --- a/alcs/acs2/ClassifiersList.py +++ b/alcs/acs2/ClassifiersList.py @@ -19,21 +19,6 @@ def __init__(self, seq=(), cfg=None): self.cfg = cfg list.__init__(self, seq or []) - #TODO possibly new constructor needed????? - # /** - # * Constructor for match set generation. (Does not copy classifiers.) - # */ - # ClassifierList::ClassifierList(ClassifierList *pop, Perception *percept) { - # list = 0; - # size = 0; - # env = pop->env; - # for (PureClassifierList *listp = pop->list; listp != 0; listp = listp->next) { - # if (listp->cl->doesMatch(percept)) { - # addClassifier(listp->cl); - # } - # } - # } - def append(self, item): if not isinstance(item, Classifier): raise TypeError("Item should be a Classifier object") @@ -47,6 +32,14 @@ def form_match_set(cls, return cls([cl for cl in population if cl.condition.does_match(situation)], cfg) + @classmethod + def form_match_set_backwards(cls, + population, + situation: Perception, + cfg: ACS2Configuration): + return cls([cl for cl in population + if cl.does_match_backwards(situation)], cfg) + @classmethod def form_action_set(cls, population, @@ -457,7 +450,7 @@ def delete_ga_classifiers(self, population, match_set, child_no, def delete_a_classifier(self, match_set, population, randomfunc=random): """ Delete one classifier from a population """ - if len(population) == 0: # Nothing to remove + if len(population) == 0: # Nothing to remove return None cl_del = self.select_classifier_to_delete(randomfunc=randomfunc) if cl_del is not None: @@ -530,15 +523,250 @@ def find_subsumer(self, cl: Classifier, choice_func=choice) -> Classifier: return choice_func(most_general_subsumers) \ if most_general_subsumers else None - def search_goal_sequence(self, start, goal): - #TODO: Action **ClassifierList::searchGoalSequence(Perception *start, Perception *goal) { - pass - def exists_classifier(self, previous_situation, action, situation, quality): + """ + Returns True if there is a classifier in this list with a quality higher than 'quality' + that matches previous_situation, specifies action, and predicts situation. + Returns False otherwise. + :param previous_situation: + :param action: + :param situation: + :param quality: + :return: + """ for cl in self: - if cl.q > quality: - if cl.does_match(previous_situation): - if cl.has_action(action): - if cl.does_anticipate_correctly(previous_situation, situation): - return 1 - return 0 + if cl.q > quality and cl.does_match(previous_situation) and cl.action == action \ + and cl.does_anticipate_correctly(previous_situation, situation): + return True + return False + + def get_quality_classifiers_list(self, quality, cfg=None): + """ + Constructs classifier list out of a list with q > quality. + :param quality: + :param cfg: + :return: ClassifiersList with only quality classifiers. + """ + listp = ClassifiersList(cfg=cfg) + for item in self: + if item.q > quality: + listp.append(item) + return listp + + def search_goal_sequence(self, start, goal): + """ + Searches a path from start to goal using a bidirectional method in the environmental model + (i.e. the list of reliable classifiers). + :param start: Perception + :param goal: Perception + :return: Sequence of actions + """ + reliable_classifiers = self.get_quality_classifiers_list(quality=self.cfg.theta_r, cfg=self.cfg) + + if len(reliable_classifiers) < 1: + return self._empty_sequence() + + self.forward_classifiers = [] + self.backward_classifiers = [] + + self.forward_perceptions = [] + self.backward_perceptions = [] + + max_depth = 6 + forward_size = 1 + backward_size = 1 + forward_point = 0 + backward_point = 0 + action_sequence = None + + self.forward_perceptions.append(Perception(start)) + self.backward_perceptions.append(Perception(goal)) + + for depth in range(0, max_depth): + # forward step + action_sequence, forward_size_new = self._search_one_forward_step(reliable_classifiers, forward_size, + forward_point) + forward_point = forward_size + forward_size = forward_size_new + + if action_sequence is not None: + return action_sequence + + # backwards step + action_sequence, backward_size_new = self._search_one_backward_step(reliable_classifiers, backward_size, + backward_point) + backward_point = backward_size + backward_size = backward_size_new + + if action_sequence is not None: + return action_sequence + + # depth limit was reached -> return empty action sequence + return self._empty_sequence() + + def _search_one_forward_step(self, reliable_classifiers, forward_size, forward_point): + """ + Serches one step forward in the reliable_classifiers classifier list. + Returns None if nothing was found so far, a sequence with a -1 element if the search failed completely + (which is the case if the allowed array size of 10000 is reached), or the sequence if one was found. + :param reliable_classifiers: ClassifiersList + :param forward_size: int + :param forward_point: int + :return: act sequence and new forward_size + """ + size = forward_size + for i in range(forward_point, forward_size): + match_forward = self.form_match_set(population=reliable_classifiers, situation=self.forward_perceptions[i], + cfg=self.cfg) + for match_set_element in match_forward: + anticipation = match_set_element.get_best_anticipation(self.forward_perceptions[i]) + if self._does_contain_state(self.forward_perceptions, anticipation) is None: + # state not detected forward -> search in backwards + backward_sequence_idx = self._does_contain_state(self.backward_perceptions, anticipation) + if backward_sequence_idx is None: + # state neither detected backwards + self.forward_perceptions.append(anticipation) + self.forward_classifiers.append( + self._form_new_classifiers(self.forward_classifiers, i, match_set_element)) + size += 1 + if size > 10001: + logging.debug("Arrays are full") + return self._empty_sequence(), size + else: + # sequence found + return self._sequence_found_forwards(i, backward_sequence_idx, match_set_element), size + return None, size + + def _search_one_backward_step(self, reliable_classifiers, backward_size, backward_point): + """ + Searches one step backward in the reliable_classifiers classifiers list. + Returns None if nothing was found so far, a sequence with a -1 element if the search failed completely + (which is the case if the allowed array size of 10000 is reached), or the sequence if one was found. + :param reliable_classifiers: ClassifiersList + :param backward_size: int + :param backward_point: int + :return: act sequence and new backward_size + """ + size = backward_size + for i in range(backward_point, backward_size): + match_backward = self.form_match_set_backwards(population=reliable_classifiers, + situation=self.backward_perceptions[i], + cfg=self.cfg) + for match_set_el in match_backward: + anticipation = match_set_el.get_backwards_anticipation(self.backward_perceptions[i]) + if anticipation is not None and \ + self._does_contain_state(self.backward_perceptions, anticipation) is None: + # Backwards anticipation was formable but not detected backwards + forward_sequence_idx = self._does_contain_state(self.forward_perceptions, + anticipation) + if forward_sequence_idx is None: + self.backward_perceptions.append(anticipation) + self.backward_classifiers.append( + self._form_new_classifiers(self.backward_classifiers, i, match_set_el)) + size += 1 + if size > 10001: + logging.debug("Arrays are full") + return self._empty_sequence(), size + else: + return self._sequence_found_backwards(i, forward_sequence_idx, match_set_el), size + return None, size + + def _form_new_classifiers(self, classifiers_lists, i, match_set_el): + """ + Executes actions after sequence was not detected. + :param size: int + :param classifiers_lists: list of ClassifiersLists + :param i: int + :param match_set_el: Classifier + :return: new size of classifiers + """ + if i > 0: + new_classifiers = ClassifiersList(classifiers_lists[i - 1], cfg=self.cfg) + else: + new_classifiers = ClassifiersList(cfg=self.cfg) + new_classifiers.append(match_set_el) + return new_classifiers + + def _sequence_found_forwards(self, i, backward_sequence_idx, match_set_el): + """ + Forms sequence when it was found forwards. + :param i: + :param backward_sequence_idx: + :param match_set_el: Classifier + :return: act sequence + """ + # count sequence size + sequence_size = 0 + if i > 0: + sequence_size += len(self.forward_classifiers[i - 1]) + if backward_sequence_idx > 0: + sequence_size += len(self.backward_classifiers[backward_sequence_idx - 1]) + sequence_size += 1 + + # construct sequence + act_seq = [-1] * sequence_size + j = 0 + if i > 0: + for j, cl in enumerate(self.forward_classifiers[i - 1]): + act_seq[len(self.forward_classifiers[i - 1]) - j - 1] = cl.action + j += 1 + act_seq[j] = match_set_el.action + j += 1 + if backward_sequence_idx > 0: + for k, cl in enumerate(self.backward_classifiers[backward_sequence_idx - 1]): + act_seq[k + j] = cl.action + return act_seq + + def _sequence_found_backwards(self, i, forward_sequence_idx, match_set_el): + """ + Forms sequence when it was found backwards. + :param i: int + :param forward_sequence_idx: int + :param match_set_el: Classifier + :return: act sequence + """ + # count sequence size + sequence_size = 0 + if i > 0: + sequence_size += len(self.backward_classifiers[i - 1]) + if forward_sequence_idx > 0: + sequence_size += len(self.forward_classifiers[forward_sequence_idx - 1]) + sequence_size += 1 + + # construct sequence + act_seq = [-1] * sequence_size + j = 0 + if forward_sequence_idx > 0: + for j, cl in enumerate(self.forward_classifiers[forward_sequence_idx - 1]): + act_seq[len(self.forward_classifiers[forward_sequence_idx - 1]) - j - 1] = cl.action + j += 1 + act_seq[j] = match_set_el.action + j += 1 + if i > 0: + for k, cl in enumerate(self.backward_classifiers[i - 1]): + act_seq[k + j] = cl.action + return act_seq + + @staticmethod + def _does_contain_state(perceptions, state): + """ + Returns the position in the perception list where 'state' is stored or None if state is not found + :param perceptions: Perception list + :param state: Perception + :return: + """ + for i, percept in enumerate(perceptions): + if percept == state: + return i + return None + + @staticmethod + def _empty_sequence(): + """ + Returns empty sequence. + This function might be deleted in later versions of code, but it is useful if we decide to change the definition + of an empty sequence (it used to be [-1], because it was more alike the original code in C++). + :return: empty action sequence + """ + return [] + diff --git a/alcs/acs2/Condition.py b/alcs/acs2/Condition.py index 7b41464b..65db6da4 100644 --- a/alcs/acs2/Condition.py +++ b/alcs/acs2/Condition.py @@ -1,5 +1,6 @@ from random import sample +from alcs import Perception from alcs.acs2 import AbstractCondition @@ -74,3 +75,15 @@ def two_point_crossover(self, other, samplefunc=sample): for idx, el in enumerate(range(left, right)): self[el] = chromosome2[idx] other[el] = chromosome1[idx] + + def get_backwards_anticipation(self, perception): + """ + Returns the believed backwards anticipation. Hereby, the condition is treated like an effect part. + :param perception: + :return: + """ + ant = Perception(perception) + for idx, item in enumerate(self): + if item != self.cfg.classifier_wildcard: + ant[idx] = item + return ant diff --git a/alcs/acs2/Effect.py b/alcs/acs2/Effect.py index 9f5ece16..8718a0fc 100644 --- a/alcs/acs2/Effect.py +++ b/alcs/acs2/Effect.py @@ -57,3 +57,52 @@ def is_specializable(self, p0: Perception, p1: Perception) -> bool: return False return True + + def get_best_anticipation(self, perception): + """ + Returns the most probable anticipation of the effect part. + This is usually the normal anticipation. However, if PEEs are activated, the most probable + value of each attribute is taken as the anticipation. + :param perception: Perception + :return: + """ + # TODO: implement the rest after PEEs are implemented ('getBestChar' function) + ant = Perception(perception) + for idx, item in enumerate(self): + if item != self.cfg.classifier_wildcard: + ant[idx] = item + return ant + + def does_specify_only_changes_backwards(self, back_anticipation, situation): + """ + Returns if the effect part specifies at least one of the percepts. + An PEE attribute never specifies the corresponding percept. + :param back_anticipation: Perception + :param situation: Perception + :return: + """ + for idx, (item, back_ant, sit) in enumerate(zip(self, back_anticipation, situation)): + if item == self.cfg.classifier_wildcard and back_ant != sit: + # change anticipated backwards although no change should occur + return False + # TODO: if PEEs are implemented, 'isEnhanced()' should be added to the condition below + if item != self.cfg.classifier_wildcard and item == back_ant: + return False + return True + + def does_match(self, perception, other_perception): + """ + Returns if the effect matches the perception. + Hereby, the specified attributes are compared with perception. + Where the effect part has got #-symbols perception and other_perception are compared. + If they are not equal the effect part does not match. + :param perception: Perception + :param other_perception: Perception + :return: + """ + for (item, percept, percept2) in zip(self, perception, other_perception): + if item == self.cfg.classifier_wildcard and percept != percept2: + return False + elif item != self.cfg.classifier_wildcard and item != percept: + return False + return True diff --git a/assets/ACS2/ACSConstants.h b/assets/ACS2/ACSConstants.h index 5b9021d7..792fe48f 100755 --- a/assets/ACS2/ACSConstants.h +++ b/assets/ACS2/ACSConstants.h @@ -41,7 +41,7 @@ #define PROB_EXPLORATION_BIAS 0.5 /* specifies the probability of applying an exploration biased action-selection */ #define EXPLORATION_BIAS_METHOD 2 /* 0 = action delay bias, 1 = knowledge array bias, 2 = 50/50 */ -#define DO_ACTION_PLANNING 0 +#define DO_ACTION_PLANNING 1 //0 #define ACTION_PLANNING_FREQUENCY 50 /* GA constants */ @@ -56,8 +56,8 @@ #define DO_SUBSUMPTION 1 /*andere Makros:*/ -#define ENVIRONMENT_CLASS MPEnvironment -#define RESULT_FILE "ACS2_Maze4_5050B050.txt" +#define ENVIRONMENT_CLASS HandEyeEnvironment //MPEnvironment +#define RESULT_FILE "ACS2_HandEye_withAP.txt"//"ACS2_Maze4_5050B050.txt" #define MAX_STEPS 30000 #define MAX_TRIAL_STEPS 50 diff --git a/assets/ACS2/ClassifierList.cc b/assets/ACS2/ClassifierList.cc index eff7ddae..ea7363f6 100755 --- a/assets/ACS2/ClassifierList.cc +++ b/assets/ACS2/ClassifierList.cc @@ -1158,6 +1158,7 @@ Action ** ClassifierList::searchOneForwardStep(ClassifierList *relList, int *fs, int fSize, int fPoint, Perception **arrayPF, int bSize, Perception **arrayPB, ClassifierList **arrayListF, ClassifierList **arrayListB) { +// cout << "###SIZE " << fPoint << " " << fSize << "\n"; for (int i = fPoint; i < fSize; i++) { ClassifierList *matchFW = new ClassifierList(relList, arrayPF[i]); if (matchFW->size > 0) { diff --git a/assets/ACS2/Effect.cc b/assets/ACS2/Effect.cc index 63bf42a5..3d8bc973 100755 --- a/assets/ACS2/Effect.cc +++ b/assets/ACS2/Effect.cc @@ -87,10 +87,12 @@ Effect::Effect(Effect *ef1, Effect *ef2, double q1, double q2, Perception *perce */ Perception *Effect::getBestAnticipation(Perception *percept) { Perception *ant = new Perception(percept); + //cout << "List:" << list << "a: " << ant << "\n"; list->reset(); ProbCharPosItem *item = list->getNextItem(); while (item != 0) { ant->setAttribute(item->getItem()->getBestChar(), item->getPos()); + //cout << "a: " << ant << " " << item->getItem() << " " << item->getPos() << "\n"; item = list->getNextItem(); } return ant; @@ -174,6 +176,7 @@ int Effect::doesSpecifyOnlyChangesBackwards(Perception *backAnt, Perception *sit } if (item->getItem()->doesContain(backAnt->getAttribute(i)) && !item->getItem()->isEnhanced()) //if the attribute contains more values, it is not considered to specify one. + cout << "#### " << i << " " << this << " " << backAnt << " " << item->getItem() << "\n"; return 0; i++; } diff --git a/assets/ACS2/HandEyeEnvironment.h b/assets/ACS2/HandEyeEnvironment.h index dcbeedd7..8fd5996c 100755 --- a/assets/ACS2/HandEyeEnvironment.h +++ b/assets/ACS2/HandEyeEnvironment.h @@ -23,7 +23,7 @@ #include"Perception.h" #include"Action.h" -#define HE_GRID_SIZE 4 /* x size of the 2-D quadratic plane */ +#define HE_GRID_SIZE 3 /* x size of the 2-D quadratic plane */ #define NOTE_IN_HAND 1 /*specifies if feeler should switch to '2' when block is in hand */ #define HE_TEST_NO 100 /* number of tests during testing mode */ diff --git a/assets/ACS2/acs2++.cc b/assets/ACS2/acs2++.cc index ff19e212..1e695be1 100755 --- a/assets/ACS2/acs2++.cc +++ b/assets/ACS2/acs2++.cc @@ -23,7 +23,7 @@ #include "Classifier.h" #include "ClassifierList.h" #include "MazeEnvironment.h" -#include "MPEnvironment.h" +#include "HandEyeEnvironment.h" using namespace std; @@ -314,10 +314,12 @@ int startActionPlanning(ClassifierList *population, Environment *env, int time, } else { Action **actSequence = population->searchGoalSequence(situation, goalSituation); int i; - //cout<"; - //for(act=actSequence[0], i=0; act!=0; ++i, act=actSequence[i]) - // cout<"; - //cout<"; +// for(a=actSequence[0], i=0; a!=0; ++i, a=actSequence[i]) +// cout<"; +// cout< Date: Tue, 18 Sep 2018 14:36:29 +0200 Subject: [PATCH 38/83] working on TypeError: 'Perception' object does not support item assignment --- .../acs2}/handeye/__init__.py | 0 .../acs2}/handeye/acs2_in_handeye_ap.py | 19 +++++++------------ .../acs2}/handeye/acs2_in_handeye_no_ap.py | 2 +- .../acs2}/handeye/utils.py | 0 lcs/agents/acs2/Condition.py | 2 +- lcs/agents/acs2/Effect.py | 10 +++++----- 6 files changed, 14 insertions(+), 19 deletions(-) rename {integration => examples/acs2}/handeye/__init__.py (100%) rename {integration => examples/acs2}/handeye/acs2_in_handeye_ap.py (57%) rename {integration => examples/acs2}/handeye/acs2_in_handeye_no_ap.py (94%) rename {integration => examples/acs2}/handeye/utils.py (100%) diff --git a/integration/handeye/__init__.py b/examples/acs2/handeye/__init__.py similarity index 100% rename from integration/handeye/__init__.py rename to examples/acs2/handeye/__init__.py diff --git a/integration/handeye/acs2_in_handeye_ap.py b/examples/acs2/handeye/acs2_in_handeye_ap.py similarity index 57% rename from integration/handeye/acs2_in_handeye_ap.py rename to examples/acs2/handeye/acs2_in_handeye_ap.py index db93a9b7..a28d17fd 100644 --- a/integration/handeye/acs2_in_handeye_ap.py +++ b/examples/acs2/handeye/acs2_in_handeye_ap.py @@ -2,29 +2,24 @@ import gym -import sys -sys.path.append('/home/e-dzia/openai-envs') - # noinspection PyUnresolvedReferences import gym_handeye -from alcs import ACS2, ACS2Configuration -from integration.handeye.utils import calculate_performance +from lcs.agents.acs2 import ACS2, Configuration +from examples.acs2.handeye.utils import calculate_performance # Configure logger logging.basicConfig(level=logging.INFO) - if __name__ == '__main__': - # Load desired environment hand_eye = gym.make('HandEye3-v0') # Configure and create the agent - cfg = ACS2Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, - epsilon=1.0, - do_ga=False, - do_action_planning=True, - performance_fcn=calculate_performance) + cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, + epsilon=1.0, + do_ga=False, + do_action_planning=True, + performance_fcn=calculate_performance) logging.info(cfg) # Explore the environment diff --git a/integration/handeye/acs2_in_handeye_no_ap.py b/examples/acs2/handeye/acs2_in_handeye_no_ap.py similarity index 94% rename from integration/handeye/acs2_in_handeye_no_ap.py rename to examples/acs2/handeye/acs2_in_handeye_no_ap.py index 3a085077..9f03bfa0 100644 --- a/integration/handeye/acs2_in_handeye_no_ap.py +++ b/examples/acs2/handeye/acs2_in_handeye_no_ap.py @@ -8,7 +8,7 @@ # noinspection PyUnresolvedReferences import gym_handeye from alcs import ACS2, ACS2Configuration -from integration.handeye.utils import calculate_performance +from examples.acs2.handeye.utils import calculate_performance # Configure logger logging.basicConfig(level=logging.INFO) diff --git a/integration/handeye/utils.py b/examples/acs2/handeye/utils.py similarity index 100% rename from integration/handeye/utils.py rename to examples/acs2/handeye/utils.py diff --git a/lcs/agents/acs2/Condition.py b/lcs/agents/acs2/Condition.py index 9fe21048..fd55199f 100644 --- a/lcs/agents/acs2/Condition.py +++ b/lcs/agents/acs2/Condition.py @@ -77,6 +77,6 @@ def get_backwards_anticipation(self, perception): """ ant = Perception(perception) for idx, item in enumerate(self): - if item != self.cfg.classifier_wildcard: + if item != self.wildcard: ant[idx] = item return ant diff --git a/lcs/agents/acs2/Effect.py b/lcs/agents/acs2/Effect.py index 86ea812f..219d8a8d 100644 --- a/lcs/agents/acs2/Effect.py +++ b/lcs/agents/acs2/Effect.py @@ -56,7 +56,7 @@ def get_best_anticipation(self, perception): # TODO: implement the rest after PEEs are implemented ('getBestChar' function) ant = Perception(perception) for idx, item in enumerate(self): - if item != self.cfg.classifier_wildcard: + if item != self.wildcard: ant[idx] = item return ant @@ -69,11 +69,11 @@ def does_specify_only_changes_backwards(self, back_anticipation, situation): :return: """ for idx, (item, back_ant, sit) in enumerate(zip(self, back_anticipation, situation)): - if item == self.cfg.classifier_wildcard and back_ant != sit: + if item == self.wildcard and back_ant != sit: # change anticipated backwards although no change should occur return False # TODO: if PEEs are implemented, 'isEnhanced()' should be added to the condition below - if item != self.cfg.classifier_wildcard and item == back_ant: + if item != self.wildcard and item == back_ant: return False return True @@ -88,8 +88,8 @@ def does_match(self, perception, other_perception): :return: """ for (item, percept, percept2) in zip(self, perception, other_perception): - if item == self.cfg.classifier_wildcard and percept != percept2: + if item == self.wildcard and percept != percept2: return False - elif item != self.cfg.classifier_wildcard and item != percept: + elif item != self.wildcard and item != percept: return False return True From 22fb4ecffa2e0c6fec8a0ba79ee3ff0c12b28017 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 19 Sep 2018 13:17:01 +0200 Subject: [PATCH 39/83] made some changes to be compatible with ParrotPrediction/masteR --- .../acs2/handeye/acs2_in_handeye_no_ap.py | 4 +-- lcs/Perception.py | 9 ++++++ lcs/agents/acs2/ACS2.py | 4 +-- lcs/agents/acs2/ClassifiersList.py | 3 +- lcs/agents/acs2/Condition.py | 2 +- lcs/agents/acs2/Effect.py | 2 +- tests/lcs/agents/acs2/test_Condition.py | 8 ++--- tests/lcs/agents/acs2/test_Effect.py | 32 +++++++++---------- 8 files changed, 37 insertions(+), 27 deletions(-) diff --git a/examples/acs2/handeye/acs2_in_handeye_no_ap.py b/examples/acs2/handeye/acs2_in_handeye_no_ap.py index 9f03bfa0..c4a811de 100644 --- a/examples/acs2/handeye/acs2_in_handeye_no_ap.py +++ b/examples/acs2/handeye/acs2_in_handeye_no_ap.py @@ -7,7 +7,7 @@ # noinspection PyUnresolvedReferences import gym_handeye -from alcs import ACS2, ACS2Configuration +from lcs.agents.acs2 import ACS2, Configuration from examples.acs2.handeye.utils import calculate_performance # Configure logger @@ -20,7 +20,7 @@ hand_eye = gym.make('HandEye3-v0') # Configure and create the agent - cfg = ACS2Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, + cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, epsilon=1.0, do_ga=False, do_action_planning=False, diff --git a/lcs/Perception.py b/lcs/Perception.py index b6bff224..7ff82a02 100644 --- a/lcs/Perception.py +++ b/lcs/Perception.py @@ -27,3 +27,12 @@ def __len__(self) -> int: def __repr__(self): return ' '.join(map(str, self)) + + def __setitem__(self, i, o): + self._items[i] = o + + def __eq__(self, other): + for si, oi in zip(self, other): + if si != oi: + return False + return True diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index 3a263436..4467d24b 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -252,9 +252,9 @@ def _run_action_planning(self, env, time, situation, action = act action_set = ClassifiersList.form_action_set(match_set, action, self.cfg) - raw_state, reward, done, _ = env.step(self.parse_action(action)) + raw_state, reward, done, _ = env.step(parse_action(action)) previous_situation = situation - situation = self.parse_state(raw_state) + situation = parse_state(raw_state) if not action_set.exists_classifier(previous_situation, action, situation, self.cfg.theta_r): # no reliable classifier was able to anticipate such a change diff --git a/lcs/agents/acs2/ClassifiersList.py b/lcs/agents/acs2/ClassifiersList.py index 326387c7..59d8e6b2 100644 --- a/lcs/agents/acs2/ClassifiersList.py +++ b/lcs/agents/acs2/ClassifiersList.py @@ -567,7 +567,8 @@ def _form_new_classifiers(self, classifiers_lists, i, match_set_el): :return: new size of classifiers """ if i > 0: - new_classifiers = ClassifiersList(classifiers_lists[i - 1], cfg=self.cfg) + new_classifiers = ClassifiersList(cfg=self.cfg) + new_classifiers.extend(classifiers_lists[i - 1]) # TODO else: new_classifiers = ClassifiersList(cfg=self.cfg) new_classifiers.append(match_set_el) diff --git a/lcs/agents/acs2/Condition.py b/lcs/agents/acs2/Condition.py index fd55199f..72c937de 100644 --- a/lcs/agents/acs2/Condition.py +++ b/lcs/agents/acs2/Condition.py @@ -78,5 +78,5 @@ def get_backwards_anticipation(self, perception): ant = Perception(perception) for idx, item in enumerate(self): if item != self.wildcard: - ant[idx] = item + ant[idx] = item # TODO return ant diff --git a/lcs/agents/acs2/Effect.py b/lcs/agents/acs2/Effect.py index 219d8a8d..348f73de 100644 --- a/lcs/agents/acs2/Effect.py +++ b/lcs/agents/acs2/Effect.py @@ -68,7 +68,7 @@ def does_specify_only_changes_backwards(self, back_anticipation, situation): :param situation: Perception :return: """ - for idx, (item, back_ant, sit) in enumerate(zip(self, back_anticipation, situation)): + for item, back_ant, sit in zip(self, back_anticipation, situation): if item == self.wildcard and back_ant != sit: # change anticipated backwards although no change should occur return False diff --git a/tests/lcs/agents/acs2/test_Condition.py b/tests/lcs/agents/acs2/test_Condition.py index 21d4bffc..0148203f 100644 --- a/tests/lcs/agents/acs2/test_Condition.py +++ b/tests/lcs/agents/acs2/test_Condition.py @@ -136,10 +136,10 @@ def test_should_match_condition(self, _c, _other, _result): # then assert c.does_match(other) is _result - def test_get_backwards_anticipation(self, cfg): + def test_get_backwards_anticipation(self): # given p0 = Perception(['1', '1', '1', '1', '1', '0', '1', '1']) - condition = Condition(['#', '#', '0', '#', '#', '1', '#', '#'], cfg) + condition = Condition(['#', '#', '0', '#', '#', '1', '#', '#']) # when result = condition.get_backwards_anticipation(p0) @@ -147,10 +147,10 @@ def test_get_backwards_anticipation(self, cfg): # then assert result == ['1', '1', '0', '1', '1', '1', '1', '1'] - def test_get_backwards_anticipation_2(self, cfg): + def test_get_backwards_anticipation_2(self): # given p0 = Perception(['0', '1', '1', '1', '1', '0', '1', '0']) - condition = Condition(['#', '0', '#', '#', '#', '1', '#', '#'], cfg) + condition = Condition(['#', '0', '#', '#', '#', '1', '#', '#']) # when result = condition.get_backwards_anticipation(p0) diff --git a/tests/lcs/agents/acs2/test_Effect.py b/tests/lcs/agents/acs2/test_Effect.py index 9137c329..671e492f 100644 --- a/tests/lcs/agents/acs2/test_Effect.py +++ b/tests/lcs/agents/acs2/test_Effect.py @@ -116,11 +116,11 @@ def test_eq(self): assert Effect('00001111') == Effect('00001111') assert Effect('00001111') != Effect('0000111#') - def test_does_match(self, cfg): + def test_does_match(self): # given p0 = Perception(['1', '1', '0', '1', '1', '1', '0', '1']) p1 = Perception(['1', '1', '1', '1', '1', '0', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#'], cfg) + effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) # when result = effect.does_match(p0, p1) @@ -128,11 +128,11 @@ def test_does_match(self, cfg): # then assert result is True - def test_does_match_false(self, cfg): + def test_does_match_false(self): # given p0 = Perception(['1', '0', '1', '1', '1', '0', '0', '1']) p1 = Perception(['1', '1', '0', '1', '1', '1', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#'], cfg) + effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) # when result = effect.does_match(p0, p1) @@ -140,11 +140,11 @@ def test_does_match_false(self, cfg): # then assert result is False - def test_does_match_false_2(self, cfg): + def test_does_match_false_2(self): # given p0 = Perception(['1', '1', '1', '1', '0', '0', '0', '1']) p1 = Perception(['1', '1', '0', '1', '1', '1', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#'], cfg) + effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) # when result = effect.does_match(p0, p1) @@ -152,11 +152,11 @@ def test_does_match_false_2(self, cfg): # then assert result is False - def test_does_match_false_3(self, cfg): + def test_does_match_false_3(self): # given p0 = Perception(['1', '1', '1', '1', '1', '0', '1', '1']) p1 = Perception(['1', '1', '1', '1', '1', '1', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#'], cfg) + effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) # when result = effect.does_match(p0, p1) @@ -164,11 +164,11 @@ def test_does_match_false_3(self, cfg): # then assert result is False - def test_get_best_anticipation(self, cfg): + def test_get_best_anticipation(self): # given p0 = Perception(['1', '1', '0', '1', '1', '1', '1', '1']) p1 = Perception(['1', '1', '1', '1', '1', '1', '1', '1']) - effect = Effect(['#', '#', '0', '#', '#', '0', '#', '#'], cfg) + effect = Effect(['#', '#', '0', '#', '#', '0', '#', '#']) # when result0 = effect.get_best_anticipation(p0) @@ -178,11 +178,11 @@ def test_get_best_anticipation(self, cfg): assert result0 == ['1', '1', '0', '1', '1', '0', '1', '1'] assert result1 == ['1', '1', '0', '1', '1', '0', '1', '1'] - def test_does_specify_only_changes_backwards(self, cfg): + def test_does_specify_only_changes_backwards(self): # given back_ant = Perception(['1', '1', '0', '1', '1', '1', '1', '0']) sit = Perception(['1', '1', '1', '1', '1', '1', '1', '0']) - effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#'], cfg) + effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#']) # when result = effect.does_specify_only_changes_backwards(back_ant, sit) @@ -190,11 +190,11 @@ def test_does_specify_only_changes_backwards(self, cfg): # then assert result is True - def test_does_specify_only_changes_backwards_false(self, cfg): + def test_does_specify_only_changes_backwards_false(self): # given back_ant = Perception(['1', '1', '0', '1', '1', '1', '1', '0']) sit = Perception(['1', '1', '1', '1', '1', '1', '0', '0']) - effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#'], cfg) + effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#']) # when result = effect.does_specify_only_changes_backwards(back_ant, sit) @@ -202,11 +202,11 @@ def test_does_specify_only_changes_backwards_false(self, cfg): # then assert result is False - def test_does_specify_only_changes_backwards_false_2(self, cfg): + def test_does_specify_only_changes_backwards_false_2(self): # given back_ant = Perception(['1', '1', '0', '1', '1', '0', '1', '0']) sit = Perception(['1', '1', '1', '1', '1', '1', '1', '0']) - effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#'], cfg) + effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#']) # when result = effect.does_specify_only_changes_backwards(back_ant, sit) From dd23a8a5ef62448cc4497efd2e94defbb2062ccb Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 19 Sep 2018 13:51:35 +0200 Subject: [PATCH 40/83] undid some changes in original C++ code --- assets/ACS2/ClassifierList.cc | 1 - assets/ACS2/Effect.cc | 3 --- assets/ACS2/acs2++.cc | 9 --------- 3 files changed, 13 deletions(-) diff --git a/assets/ACS2/ClassifierList.cc b/assets/ACS2/ClassifierList.cc index ea7363f6..eff7ddae 100755 --- a/assets/ACS2/ClassifierList.cc +++ b/assets/ACS2/ClassifierList.cc @@ -1158,7 +1158,6 @@ Action ** ClassifierList::searchOneForwardStep(ClassifierList *relList, int *fs, int fSize, int fPoint, Perception **arrayPF, int bSize, Perception **arrayPB, ClassifierList **arrayListF, ClassifierList **arrayListB) { -// cout << "###SIZE " << fPoint << " " << fSize << "\n"; for (int i = fPoint; i < fSize; i++) { ClassifierList *matchFW = new ClassifierList(relList, arrayPF[i]); if (matchFW->size > 0) { diff --git a/assets/ACS2/Effect.cc b/assets/ACS2/Effect.cc index 3d8bc973..63bf42a5 100755 --- a/assets/ACS2/Effect.cc +++ b/assets/ACS2/Effect.cc @@ -87,12 +87,10 @@ Effect::Effect(Effect *ef1, Effect *ef2, double q1, double q2, Perception *perce */ Perception *Effect::getBestAnticipation(Perception *percept) { Perception *ant = new Perception(percept); - //cout << "List:" << list << "a: " << ant << "\n"; list->reset(); ProbCharPosItem *item = list->getNextItem(); while (item != 0) { ant->setAttribute(item->getItem()->getBestChar(), item->getPos()); - //cout << "a: " << ant << " " << item->getItem() << " " << item->getPos() << "\n"; item = list->getNextItem(); } return ant; @@ -176,7 +174,6 @@ int Effect::doesSpecifyOnlyChangesBackwards(Perception *backAnt, Perception *sit } if (item->getItem()->doesContain(backAnt->getAttribute(i)) && !item->getItem()->isEnhanced()) //if the attribute contains more values, it is not considered to specify one. - cout << "#### " << i << " " << this << " " << backAnt << " " << item->getItem() << "\n"; return 0; i++; } diff --git a/assets/ACS2/acs2++.cc b/assets/ACS2/acs2++.cc index 1e695be1..8077e030 100755 --- a/assets/ACS2/acs2++.cc +++ b/assets/ACS2/acs2++.cc @@ -298,7 +298,6 @@ int startOneTrialExploit(ClassifierList *population, Environment *env) { int startActionPlanning(ClassifierList *population, Environment *env, int time, ofstream *out, Perception *situation, Perception *previousSituation, ClassifierList **actionSet, Action *act, double *rho0) { /* Recheck this function -> is step set correct and we need to do model testing in here! */ - //Perception - Perception *goalSituation = new Perception(); int steps; @@ -307,19 +306,11 @@ int startActionPlanning(ClassifierList *population, Environment *env, int time, for (steps = 0; !env->isReset() && (REWARD_TEST || time + steps <= MAX_STEPS) && (!REWARD_TEST || steps < MAX_TRIAL_STEPS);) { - //take new goal from environment if (!env->getGoalState(goalSituation)) { - //if there is none,then break break; } else { Action **actSequence = population->searchGoalSequence(situation, goalSituation); int i; - Action *a; - // TODO go back to comment -// cout<"; -// for(a=actSequence[0], i=0; a!=0; ++i, a=actSequence[i]) -// cout<"; -// cout< Date: Wed, 19 Sep 2018 14:15:14 +0200 Subject: [PATCH 41/83] deleted unused imports from examples/handeye --- examples/acs2/handeye/acs2_in_handeye_no_ap.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/examples/acs2/handeye/acs2_in_handeye_no_ap.py b/examples/acs2/handeye/acs2_in_handeye_no_ap.py index c4a811de..e482c5e1 100644 --- a/examples/acs2/handeye/acs2_in_handeye_no_ap.py +++ b/examples/acs2/handeye/acs2_in_handeye_no_ap.py @@ -2,9 +2,6 @@ import gym -import sys -sys.path.append('/home/e-dzia/openai-envs') - # noinspection PyUnresolvedReferences import gym_handeye from lcs.agents.acs2 import ACS2, Configuration @@ -13,9 +10,7 @@ # Configure logger logging.basicConfig(level=logging.INFO) - if __name__ == '__main__': - # Load desired environment hand_eye = gym.make('HandEye3-v0') From 93bab07e6b350657c92f5cc156544148a546a3d5 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 19 Sep 2018 15:42:18 +0200 Subject: [PATCH 42/83] fixed line length --- .../acs2/handeye/acs2_in_handeye_no_ap.py | 8 +- lcs/agents/acs2/ACS2.py | 50 ++++-- lcs/agents/acs2/Classifier.py | 26 +-- lcs/agents/acs2/ClassifiersList.py | 165 +++++++++++------- lcs/agents/acs2/Condition.py | 3 +- lcs/agents/acs2/Effect.py | 21 ++- tests/lcs/agents/acs2/test_ClassifierList.py | 25 ++- 7 files changed, 191 insertions(+), 107 deletions(-) diff --git a/examples/acs2/handeye/acs2_in_handeye_no_ap.py b/examples/acs2/handeye/acs2_in_handeye_no_ap.py index e482c5e1..c97b5f68 100644 --- a/examples/acs2/handeye/acs2_in_handeye_no_ap.py +++ b/examples/acs2/handeye/acs2_in_handeye_no_ap.py @@ -16,10 +16,10 @@ # Configure and create the agent cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, - epsilon=1.0, - do_ga=False, - do_action_planning=False, - performance_fcn=calculate_performance) + epsilon=1.0, + do_ga=False, + do_action_planning=False, + performance_fcn=calculate_performance) logging.info(cfg) # Explore the environment diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index 4467d24b..21473f38 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -11,7 +11,7 @@ class ACS2(Agent): def __init__(self, cfg: Configuration, - population: ClassifiersList=None) -> None: + population: ClassifiersList = None) -> None: self.cfg = cfg if population: @@ -104,9 +104,13 @@ def _run_trial_explore(self, env, time, current_trial=None): done = False while not done: - if self.cfg.do_action_planning and (steps + time) % self.cfg.action_planning_frequency == 0: + if self.cfg.do_action_planning and \ + (steps + time) % self.cfg.action_planning_frequency == 0: # Action Planning for increased model learning - steps_ap, state, prev_state, action_set, reward = self._run_action_planning(env, steps + time, state, prev_state, action_set, action, reward) + steps_ap, state, prev_state, action_set, reward = \ + self._run_action_planning(env, steps + time, state, + prev_state, action_set, action, + reward) steps += steps_ap match_set = self.population.form_match_set(state, @@ -206,9 +210,11 @@ def _run_action_planning(self, env, time, situation, previous_situation, action_set, action, reward): """ Executes action planning for model learning speed up. - Method requests goals from 'goal generator' provided by the environment. - If goal is provided, ACS2 searches for a goal sequence in the current model (only the reliable classifiers). - This is done as long as goals are provided and ACS2 finds a sequence and successfully reaches the goal. + Method requests goals from 'goal generator' provided by + the environment. If goal is provided, ACS2 searches for + a goal sequence in the current model (only the reliable classifiers). + This is done as long as goals are provided and ACS2 finds a sequence + and successfully reaches the goal. :param env: :param time: :param situation: @@ -222,7 +228,8 @@ def _run_action_planning(self, env, time, situation, # The environment has to have a function "get_goal_state" if not hasattr(env.env, "get_goal_state"): - logging.debug("Action planning stopped - no function get_goal_state in env") + logging.debug("Action planning stopped - " + "no function get_goal_state in env") return 0, situation, previous_situation, action_set, reward steps = 0 @@ -234,7 +241,8 @@ def _run_action_planning(self, env, time, situation, if goal_situation is None: break - act_sequence = self.population.search_goal_sequence(situation, goal_situation) + act_sequence = self.population.search_goal_sequence(situation, + goal_situation) # Execute the found sequence and learn during executing i = 0 @@ -242,22 +250,34 @@ def _run_action_planning(self, env, time, situation, if act == -1: break - match_set = self.population.form_match_set(situation=situation, cfg=self.cfg) + match_set = self.population.form_match_set(situation=situation, + cfg=self.cfg) if action_set is not None and previous_situation is not None: - action_set.apply_alp(previous_situation, action, situation, time + steps, self.population, match_set) - action_set.apply_reinforcement_learning(reward, match_set.get_maximum_fitness()) + action_set.apply_alp(previous_situation, action, situation, + time + steps, self.population, + match_set) + action_set.\ + apply_reinforcement_learning(reward, + match_set. + get_maximum_fitness()) if self.cfg.do_ga: - action_set.apply_ga(time + steps, self.population, match_set, situation) + action_set.apply_ga(time + steps, self.population, + match_set, situation) action = act - action_set = ClassifiersList.form_action_set(match_set, action, self.cfg) + action_set = ClassifiersList.form_action_set(match_set, action, + self.cfg) raw_state, reward, done, _ = env.step(parse_action(action)) previous_situation = situation situation = parse_state(raw_state) - if not action_set.exists_classifier(previous_situation, action, situation, self.cfg.theta_r): - # no reliable classifier was able to anticipate such a change + if not action_set.exists_classifier(previous_situation, + action, + situation, + self.cfg.theta_r): + # no reliable classifier was able to anticipate + # such a change break steps += 1 diff --git a/lcs/agents/acs2/Classifier.py b/lcs/agents/acs2/Classifier.py index 92ca11a5..9d7704ce 100644 --- a/lcs/agents/acs2/Classifier.py +++ b/lcs/agents/acs2/Classifier.py @@ -408,7 +408,8 @@ def does_match(self, situation): def does_match_backwards(self, situation): """ Returns if 'situation' is matched by the anticipations. - This is only the case if the specified conditions that have #-symbols in the effect part are also matched! + This is only the case if the specified conditions that have #-symbols + in the effect part are also matched! :param situation: :return: """ @@ -419,8 +420,9 @@ def does_match_backwards(self, situation): def get_best_anticipation(self, perception): """ - Returns the anticipation, the classifier believes to happen most probably. - This is usually the normal anticipation. However, if PEEs are activated, the most probable + Returns the anticipation, the classifier believes to happen most + probably. This is usually the normal anticipation. + However, if PEEs are activated, the most probable value of each attribute is returned. :param perception: Perception :return: @@ -430,15 +432,19 @@ def get_best_anticipation(self, perception): def get_backwards_anticipation(self, perception): """ Returns the backwards anticipation. - Returns -1 if the backwards anticipation was impossible to create. This is the case if - changing attributes are not specified in the conditions. + Returns -1 if the backwards anticipation was impossible to create. + This is the case if changing attributes are not specified + in the conditions. :param perception: :return: """ - back_anticipation = self.condition.get_backwards_anticipation(perception) - if not self.effect.does_specify_only_changes_backwards(back_anticipation, perception): - # If a specified attribute in the effect part matches the anticipated 'back_anticipation', - # the backward anticipation fails, because a specified attribute in Effect means a change! + back_anticipation = self.condition.\ + get_backwards_anticipation(perception) + if not self.effect.\ + does_specify_only_changes_backwards(back_anticipation, + perception): + # If a specified attribute in the effect part matches + # the anticipated 'back_anticipation', the backward anticipation + # fails, because a specified attribute in Effect means a change! return None return back_anticipation - diff --git a/lcs/agents/acs2/ClassifiersList.py b/lcs/agents/acs2/ClassifiersList.py index 59d8e6b2..137bfeb3 100644 --- a/lcs/agents/acs2/ClassifiersList.py +++ b/lcs/agents/acs2/ClassifiersList.py @@ -17,9 +17,10 @@ class ClassifiersList(TypedList): """ Represents overall population, match/action sets """ + def __init__(self, *args, cfg: Configuration) -> None: self.cfg = cfg - super().__init__((Classifier, ), *args) + super().__init__((Classifier,), *args) def form_match_set(self, situation: Perception, @@ -333,7 +334,7 @@ def delete_a_classifier(self, population: ClassifiersList, randomfunc=random): """ Delete one classifier from a population """ - if len(population) == 0: # Nothing to remove + if len(population) == 0: # Nothing to remove return None cl_del = self.select_classifier_to_delete(randomfunc=randomfunc) if cl_del is not None: @@ -410,10 +411,12 @@ def find_subsumer(self, cl: Classifier, choice_func=choice) -> \ return choice_func(most_general_subsumers) \ if most_general_subsumers else None - def exists_classifier(self, previous_situation, action, situation, quality): + def exists_classifier(self, previous_situation, action, situation, + quality): """ - Returns True if there is a classifier in this list with a quality higher than 'quality' - that matches previous_situation, specifies action, and predicts situation. + Returns True if there is a classifier in this list with a quality + higher than 'quality' that matches previous_situation, + specifies action, and predicts situation. Returns False otherwise. :param previous_situation: :param action: @@ -422,8 +425,10 @@ def exists_classifier(self, previous_situation, action, situation, quality): :return: """ for cl in self: - if cl.q > quality and cl.does_match(previous_situation) and cl.action == action \ - and cl.does_anticipate_correctly(previous_situation, situation): + if cl.q > quality and cl.does_match(previous_situation) \ + and cl.action == action \ + and cl.does_anticipate_correctly(previous_situation, + situation): return True return False @@ -442,13 +447,15 @@ def get_quality_classifiers_list(self, quality, cfg=None): def search_goal_sequence(self, start, goal): """ - Searches a path from start to goal using a bidirectional method in the environmental model - (i.e. the list of reliable classifiers). + Searches a path from start to goal using a bidirectional method in the + environmental model (i.e. the list of reliable classifiers). :param start: Perception :param goal: Perception :return: Sequence of actions """ - reliable_classifiers = self.get_quality_classifiers_list(quality=self.cfg.theta_r, cfg=self.cfg) + reliable_classifiers = self. \ + get_quality_classifiers_list(quality=self.cfg.theta_r, + cfg=self.cfg) if len(reliable_classifiers) < 1: return self._empty_sequence() @@ -471,8 +478,9 @@ def search_goal_sequence(self, start, goal): for depth in range(0, max_depth): # forward step - action_sequence, forward_size_new = self._search_one_forward_step(reliable_classifiers, forward_size, - forward_point) + action_sequence, forward_size_new = self. \ + _search_one_forward_step(reliable_classifiers, forward_size, + forward_point) forward_point = forward_size forward_size = forward_size_new @@ -480,8 +488,9 @@ def search_goal_sequence(self, start, goal): return action_sequence # backwards step - action_sequence, backward_size_new = self._search_one_backward_step(reliable_classifiers, backward_size, - backward_point) + action_sequence, backward_size_new = self. \ + _search_one_backward_step(reliable_classifiers, backward_size, + backward_point) backward_point = backward_size backward_size = backward_size_new @@ -491,11 +500,14 @@ def search_goal_sequence(self, start, goal): # depth limit was reached -> return empty action sequence return self._empty_sequence() - def _search_one_forward_step(self, reliable_classifiers, forward_size, forward_point): + def _search_one_forward_step(self, reliable_classifiers, forward_size, + forward_point): """ Serches one step forward in the reliable_classifiers classifier list. - Returns None if nothing was found so far, a sequence with a -1 element if the search failed completely - (which is the case if the allowed array size of 10000 is reached), or the sequence if one was found. + Returns None if nothing was found so far, a sequence with a -1 element + if the search failed completely + (which is the case if the allowed array size of 10000 is reached), + or the sequence if one was found. :param reliable_classifiers: ClassifiersList :param forward_size: int :param forward_point: int @@ -503,32 +515,45 @@ def _search_one_forward_step(self, reliable_classifiers, forward_size, forward_p """ size = forward_size for i in range(forward_point, forward_size): - match_forward = reliable_classifiers.form_match_set(situation=self.forward_perceptions[i], - cfg=self.cfg) + match_forward = reliable_classifiers. \ + form_match_set(situation=self.forward_perceptions[i], + cfg=self.cfg) for match_set_element in match_forward: - anticipation = match_set_element.get_best_anticipation(self.forward_perceptions[i]) - if self._does_contain_state(self.forward_perceptions, anticipation) is None: - # state not detected forward -> search in backwards - backward_sequence_idx = self._does_contain_state(self.backward_perceptions, anticipation) - if backward_sequence_idx is None: - # state neither detected backwards - self.forward_perceptions.append(anticipation) - self.forward_classifiers.append( - self._form_new_classifiers(self.forward_classifiers, i, match_set_element)) - size += 1 - if size > 10001: - # logging.debug("Arrays are full") - return self._empty_sequence(), size - else: - # sequence found - return self._sequence_found_forwards(i, backward_sequence_idx, match_set_element), size + anticipation = match_set_element. \ + get_best_anticipation(self.forward_perceptions[i]) + if self._does_contain_state(self.forward_perceptions, + anticipation) is None: + # state not detected forward -> search in backwards + backward_sequence_idx = self. \ + _does_contain_state(self.backward_perceptions, + anticipation) + if backward_sequence_idx is None: + # state neither detected backwards + self.forward_perceptions.append(anticipation) + self.forward_classifiers.append( + self._form_new_classifiers( + self.forward_classifiers, i, + match_set_element)) + size += 1 + if size > 10001: + # logging.debug("Arrays are full") + return self._empty_sequence(), size + else: + # sequence found + return self. \ + _sequence_found_forwards( + i, backward_sequence_idx, + match_set_element), size return None, size - def _search_one_backward_step(self, reliable_classifiers, backward_size, backward_point): + def _search_one_backward_step(self, reliable_classifiers, backward_size, + backward_point): """ - Searches one step backward in the reliable_classifiers classifiers list. - Returns None if nothing was found so far, a sequence with a -1 element if the search failed completely - (which is the case if the allowed array size of 10000 is reached), or the sequence if one was found. + Searches one step backward in the reliable_classifiers classifiers list + Returns None if nothing was found so far, a sequence with a -1 element + if the search failed completely + (which is the case if the allowed array size of 10000 is reached), + or the sequence if one was found. :param reliable_classifiers: ClassifiersList :param backward_size: int :param backward_point: int @@ -536,31 +561,40 @@ def _search_one_backward_step(self, reliable_classifiers, backward_size, backwar """ size = backward_size for i in range(backward_point, backward_size): - match_backward = reliable_classifiers.form_match_set_backwards(situation=self.backward_perceptions[i], - cfg=self.cfg) + match_backward = reliable_classifiers. \ + form_match_set_backwards( + situation=self.backward_perceptions[i], + cfg=self.cfg) for match_set_el in match_backward: - anticipation = match_set_el.get_backwards_anticipation(self.backward_perceptions[i]) + anticipation = match_set_el. \ + get_backwards_anticipation(self.backward_perceptions[i]) if anticipation is not None and \ - self._does_contain_state(self.backward_perceptions, anticipation) is None: - # Backwards anticipation was formable but not detected backwards - forward_sequence_idx = self._does_contain_state(self.forward_perceptions, - anticipation) + self._does_contain_state(self.backward_perceptions, + anticipation) is None: + # Backwards anticipation was formable but + # not detected backwards + forward_sequence_idx = self. \ + _does_contain_state(self.forward_perceptions, + anticipation) if forward_sequence_idx is None: self.backward_perceptions.append(anticipation) self.backward_classifiers.append( - self._form_new_classifiers(self.backward_classifiers, i, match_set_el)) + self._form_new_classifiers( + self.backward_classifiers, i, match_set_el)) size += 1 if size > 10001: # logging.debug("Arrays are full") return self._empty_sequence(), size else: - return self._sequence_found_backwards(i, forward_sequence_idx, match_set_el), size + return self. \ + _sequence_found_backwards( + i, forward_sequence_idx, + match_set_el), size return None, size def _form_new_classifiers(self, classifiers_lists, i, match_set_el): """ Executes actions after sequence was not detected. - :param size: int :param classifiers_lists: list of ClassifiersLists :param i: int :param match_set_el: Classifier @@ -568,7 +602,7 @@ def _form_new_classifiers(self, classifiers_lists, i, match_set_el): """ if i > 0: new_classifiers = ClassifiersList(cfg=self.cfg) - new_classifiers.extend(classifiers_lists[i - 1]) # TODO + new_classifiers.extend(classifiers_lists[i - 1]) else: new_classifiers = ClassifiersList(cfg=self.cfg) new_classifiers.append(match_set_el) @@ -587,7 +621,8 @@ def _sequence_found_forwards(self, i, backward_sequence_idx, match_set_el): if i > 0: sequence_size += len(self.forward_classifiers[i - 1]) if backward_sequence_idx > 0: - sequence_size += len(self.backward_classifiers[backward_sequence_idx - 1]) + sequence_size += len( + self.backward_classifiers[backward_sequence_idx - 1]) sequence_size += 1 # construct sequence @@ -595,12 +630,14 @@ def _sequence_found_forwards(self, i, backward_sequence_idx, match_set_el): j = 0 if i > 0: for j, cl in enumerate(self.forward_classifiers[i - 1]): - act_seq[len(self.forward_classifiers[i - 1]) - j - 1] = cl.action + act_seq[len(self.forward_classifiers[i - 1]) - j - 1] \ + = cl.action j += 1 act_seq[j] = match_set_el.action j += 1 if backward_sequence_idx > 0: - for k, cl in enumerate(self.backward_classifiers[backward_sequence_idx - 1]): + for k, cl in enumerate( + self.backward_classifiers[backward_sequence_idx - 1]): act_seq[k + j] = cl.action return act_seq @@ -617,15 +654,21 @@ def _sequence_found_backwards(self, i, forward_sequence_idx, match_set_el): if i > 0: sequence_size += len(self.backward_classifiers[i - 1]) if forward_sequence_idx > 0: - sequence_size += len(self.forward_classifiers[forward_sequence_idx - 1]) + sequence_size += len( + self.forward_classifiers[forward_sequence_idx - 1]) sequence_size += 1 # construct sequence act_seq = [-1] * sequence_size j = 0 if forward_sequence_idx > 0: - for j, cl in enumerate(self.forward_classifiers[forward_sequence_idx - 1]): - act_seq[len(self.forward_classifiers[forward_sequence_idx - 1]) - j - 1] = cl.action + for j, cl in enumerate( + self.forward_classifiers[forward_sequence_idx - 1]): + act_seq[ + len(self. + forward_classifiers[ + forward_sequence_idx - 1]) - j - 1] \ + = cl.action j += 1 act_seq[j] = match_set_el.action j += 1 @@ -637,7 +680,8 @@ def _sequence_found_backwards(self, i, forward_sequence_idx, match_set_el): @staticmethod def _does_contain_state(perceptions, state): """ - Returns the position in the perception list where 'state' is stored or None if state is not found + Returns the position in the perception list where 'state' is stored or + None if state is not found :param perceptions: Perception list :param state: Perception :return: @@ -651,9 +695,10 @@ def _does_contain_state(perceptions, state): def _empty_sequence(): """ Returns empty sequence. - This function might be deleted in later versions of code, but it is useful if we decide to change the definition - of an empty sequence (it used to be [-1], because it was more alike the original code in C++). + This function might be deleted in later versions of code, but it is + useful if we decide to change the definition + of an empty sequence (it used to be [-1], because it was more alike + the original code in C++). :return: empty action sequence """ return [] - diff --git a/lcs/agents/acs2/Condition.py b/lcs/agents/acs2/Condition.py index 72c937de..605ba818 100644 --- a/lcs/agents/acs2/Condition.py +++ b/lcs/agents/acs2/Condition.py @@ -71,7 +71,8 @@ def does_match(self, other: Union[Perception, Condition]) -> bool: def get_backwards_anticipation(self, perception): """ - Returns the believed backwards anticipation. Hereby, the condition is treated like an effect part. + Returns the believed backwards anticipation. Hereby, the condition + is treated like an effect part. :param perception: :return: """ diff --git a/lcs/agents/acs2/Effect.py b/lcs/agents/acs2/Effect.py index 348f73de..1fc7738e 100644 --- a/lcs/agents/acs2/Effect.py +++ b/lcs/agents/acs2/Effect.py @@ -48,19 +48,22 @@ def is_specializable(self, p0: Perception, p1: Perception) -> bool: def get_best_anticipation(self, perception): """ Returns the most probable anticipation of the effect part. - This is usually the normal anticipation. However, if PEEs are activated, the most probable - value of each attribute is taken as the anticipation. + This is usually the normal anticipation. However, if PEEs are + activated, the most probable value of each attribute is + taken as the anticipation. :param perception: Perception :return: """ - # TODO: implement the rest after PEEs are implemented ('getBestChar' function) + # TODO: implement the rest after PEEs are implemented + # ('getBestChar' function) ant = Perception(perception) for idx, item in enumerate(self): if item != self.wildcard: ant[idx] = item return ant - def does_specify_only_changes_backwards(self, back_anticipation, situation): + def does_specify_only_changes_backwards(self, back_anticipation, + situation): """ Returns if the effect part specifies at least one of the percepts. An PEE attribute never specifies the corresponding percept. @@ -72,7 +75,8 @@ def does_specify_only_changes_backwards(self, back_anticipation, situation): if item == self.wildcard and back_ant != sit: # change anticipated backwards although no change should occur return False - # TODO: if PEEs are implemented, 'isEnhanced()' should be added to the condition below + # TODO: if PEEs are implemented, 'isEnhanced()' should be added + # to the condition below if item != self.wildcard and item == back_ant: return False return True @@ -81,13 +85,14 @@ def does_match(self, perception, other_perception): """ Returns if the effect matches the perception. Hereby, the specified attributes are compared with perception. - Where the effect part has got #-symbols perception and other_perception are compared. - If they are not equal the effect part does not match. + Where the effect part has got #-symbols perception and other_perception + are compared. If they are not equal the effect part does not match. :param perception: Perception :param other_perception: Perception :return: """ - for (item, percept, percept2) in zip(self, perception, other_perception): + for (item, percept, percept2) in zip(self, perception, + other_perception): if item == self.wildcard and percept != percept2: return False elif item != self.wildcard and item != percept: diff --git a/tests/lcs/agents/acs2/test_ClassifierList.py b/tests/lcs/agents/acs2/test_ClassifierList.py index f52e248a..333942c0 100644 --- a/tests/lcs/agents/acs2/test_ClassifierList.py +++ b/tests/lcs/agents/acs2/test_ClassifierList.py @@ -1123,19 +1123,24 @@ def test_exists_classifier(self, cfg): q = 0.5 # C1 - OK - c1 = Classifier(condition='0##0####', action=0, effect='1##1####', quality=0.7, cfg=cfg) + c1 = Classifier(condition='0##0####', action=0, effect='1##1####', + quality=0.7, cfg=cfg) # C2 - wrong action - c2 = Classifier(condition='0##0####', action=1, effect='1##1####', quality=0.7, cfg=cfg) + c2 = Classifier(condition='0##0####', action=1, effect='1##1####', + quality=0.7, cfg=cfg) # C3 - wrong condition - c3 = Classifier(condition='0##1####', action=0, effect='1##1####', quality=0.7, cfg=cfg) + c3 = Classifier(condition='0##1####', action=0, effect='1##1####', + quality=0.7, cfg=cfg) # C4 - wrong effect - c4 = Classifier(condition='0##0####', action=0, effect='1##0####', quality=0.7, cfg=cfg) + c4 = Classifier(condition='0##0####', action=0, effect='1##0####', + quality=0.7, cfg=cfg) # C5 - wrong quality - c5 = Classifier(condition='0##0####', action=0, effect='1##1####', quality=0.25, cfg=cfg) + c5 = Classifier(condition='0##0####', action=0, effect='1##1####', + quality=0.25, cfg=cfg) population.append(c2) population.append(c3) @@ -1143,12 +1148,14 @@ def test_exists_classifier(self, cfg): population.append(c5) # when - result0 = population.exists_classifier(previous_situation=prev_situation, - situation=situation, action=act, quality=q) + result0 = population.\ + exists_classifier(previous_situation=prev_situation, + situation=situation, action=act, quality=q) population.append(c1) - result1 = population.exists_classifier(previous_situation=prev_situation, - situation=situation, action=act, quality=q) + result1 = population.\ + exists_classifier(previous_situation=prev_situation, + situation=situation, action=act, quality=q) # then assert result0 is False From 136c5ffdfd29cc5f5ebaff2eea253f90dc2d995d Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 19 Sep 2018 15:53:45 +0200 Subject: [PATCH 43/83] added strategies/action_planning.py --- lcs/strategies/action_planning.py | 261 ++++++++++++++++++++++++++++++ 1 file changed, 261 insertions(+) create mode 100644 lcs/strategies/action_planning.py diff --git a/lcs/strategies/action_planning.py b/lcs/strategies/action_planning.py new file mode 100644 index 00000000..4707b66f --- /dev/null +++ b/lcs/strategies/action_planning.py @@ -0,0 +1,261 @@ +from lcs import Perception + +forward_classifiers = [] +backward_classifiers = [] +forward_perceptions = [] +backward_perceptions = [] + + +def search_goal_sequence(reliable_classifiers, start, goal): + """ + Searches a path from start to goal using a bidirectional method in the + environmental model (i.e. the list of reliable classifiers). + :param reliable_classifiers: list of reliable classifiers + :param start: Perception + :param goal: Perception + :return: Sequence of actions + """ + if len(reliable_classifiers) < 1: + return empty_sequence() + + max_depth = 6 + forward_size = 1 + backward_size = 1 + forward_point = 0 + backward_point = 0 + action_sequence = None + + forward_perceptions.append(Perception(start)) + backward_perceptions.append(Perception(goal)) + + for depth in range(0, max_depth): + # forward step + action_sequence, forward_size_new = self. \ + _search_one_forward_step(reliable_classifiers, forward_size, + forward_point) + forward_point = forward_size + forward_size = forward_size_new + + if action_sequence is not None: + return action_sequence + + # backwards step + action_sequence, backward_size_new = self. \ + _search_one_backward_step(reliable_classifiers, backward_size, + backward_point) + backward_point = backward_size + backward_size = backward_size_new + + if action_sequence is not None: + return action_sequence + + # depth limit was reached -> return empty action sequence + return self._empty_sequence() + + +def _search_one_forward_step(self, reliable_classifiers, forward_size, + forward_point): + """ + Serches one step forward in the reliable_classifiers classifier list. + Returns None if nothing was found so far, a sequence with a -1 element + if the search failed completely + (which is the case if the allowed array size of 10000 is reached), + or the sequence if one was found. + :param reliable_classifiers: ClassifiersList + :param forward_size: int + :param forward_point: int + :return: act sequence and new forward_size + """ + size = forward_size + for i in range(forward_point, forward_size): + match_forward = reliable_classifiers. \ + form_match_set(situation=self.forward_perceptions[i], + cfg=self.cfg) + for match_set_element in match_forward: + anticipation = match_set_element. \ + get_best_anticipation(self.forward_perceptions[i]) + if self._does_contain_state(self.forward_perceptions, + anticipation) is None: + # state not detected forward -> search in backwards + backward_sequence_idx = self. \ + _does_contain_state(self.backward_perceptions, + anticipation) + if backward_sequence_idx is None: + # state neither detected backwards + self.forward_perceptions.append(anticipation) + self.forward_classifiers.append( + self._form_new_classifiers( + self.forward_classifiers, i, + match_set_element)) + size += 1 + if size > 10001: + # logging.debug("Arrays are full") + return self._empty_sequence(), size + else: + # sequence found + return self. \ + _sequence_found_forwards( + i, backward_sequence_idx, + match_set_element), size + return None, size + + +def _search_one_backward_step(self, reliable_classifiers, backward_size, + backward_point): + """ + Searches one step backward in the reliable_classifiers classifiers list + Returns None if nothing was found so far, a sequence with a -1 element + if the search failed completely + (which is the case if the allowed array size of 10000 is reached), + or the sequence if one was found. + :param reliable_classifiers: ClassifiersList + :param backward_size: int + :param backward_point: int + :return: act sequence and new backward_size + """ + size = backward_size + for i in range(backward_point, backward_size): + match_backward = reliable_classifiers. \ + form_match_set_backwards( + situation=self.backward_perceptions[i], + cfg=self.cfg) + for match_set_el in match_backward: + anticipation = match_set_el. \ + get_backwards_anticipation(self.backward_perceptions[i]) + if anticipation is not None and \ + self._does_contain_state(self.backward_perceptions, + anticipation) is None: + # Backwards anticipation was formable but + # not detected backwards + forward_sequence_idx = self. \ + _does_contain_state(self.forward_perceptions, + anticipation) + if forward_sequence_idx is None: + self.backward_perceptions.append(anticipation) + self.backward_classifiers.append( + self._form_new_classifiers( + self.backward_classifiers, i, match_set_el)) + size += 1 + if size > 10001: + # logging.debug("Arrays are full") + return self._empty_sequence(), size + else: + return self. \ + _sequence_found_backwards( + i, forward_sequence_idx, + match_set_el), size + return None, size + + +def _form_new_classifiers(self, classifiers_lists, i, match_set_el): + """ + Executes actions after sequence was not detected. + :param classifiers_lists: list of ClassifiersLists + :param i: int + :param match_set_el: Classifier + :return: new size of classifiers + """ + if i > 0: + new_classifiers = ClassifiersList(cfg=self.cfg) + new_classifiers.extend(classifiers_lists[i - 1]) + else: + new_classifiers = ClassifiersList(cfg=self.cfg) + new_classifiers.append(match_set_el) + return new_classifiers + + +def _sequence_found_forwards(self, i, backward_sequence_idx, match_set_el): + """ + Forms sequence when it was found forwards. + :param i: + :param backward_sequence_idx: + :param match_set_el: Classifier + :return: act sequence + """ + # count sequence size + sequence_size = 0 + if i > 0: + sequence_size += len(self.forward_classifiers[i - 1]) + if backward_sequence_idx > 0: + sequence_size += len( + self.backward_classifiers[backward_sequence_idx - 1]) + sequence_size += 1 + + # construct sequence + act_seq = [-1] * sequence_size + j = 0 + if i > 0: + for j, cl in enumerate(self.forward_classifiers[i - 1]): + act_seq[len(self.forward_classifiers[i - 1]) - j - 1] \ + = cl.action + j += 1 + act_seq[j] = match_set_el.action + j += 1 + if backward_sequence_idx > 0: + for k, cl in enumerate( + self.backward_classifiers[backward_sequence_idx - 1]): + act_seq[k + j] = cl.action + return act_seq + + +def _sequence_found_backwards(self, i, forward_sequence_idx, match_set_el): + """ + Forms sequence when it was found backwards. + :param i: int + :param forward_sequence_idx: int + :param match_set_el: Classifier + :return: act sequence + """ + # count sequence size + sequence_size = 0 + if i > 0: + sequence_size += len(self.backward_classifiers[i - 1]) + if forward_sequence_idx > 0: + sequence_size += len( + self.forward_classifiers[forward_sequence_idx - 1]) + sequence_size += 1 + + # construct sequence + act_seq = [-1] * sequence_size + j = 0 + if forward_sequence_idx > 0: + for j, cl in enumerate( + self.forward_classifiers[forward_sequence_idx - 1]): + act_seq[ + len(self. + forward_classifiers[ + forward_sequence_idx - 1]) - j - 1] \ + = cl.action + j += 1 + act_seq[j] = match_set_el.action + j += 1 + if i > 0: + for k, cl in enumerate(self.backward_classifiers[i - 1]): + act_seq[k + j] = cl.action + return act_seq + + +def _does_contain_state(perceptions, state): + """ + Returns the position in the perception list where 'state' is stored or + None if state is not found + :param perceptions: Perception list + :param state: Perception + :return: + """ + for i, percept in enumerate(perceptions): + if percept == state: + return i + return None + + +def empty_sequence(): + """ + Returns empty sequence. + This function might be deleted in later versions of code, but it is + useful if we decide to change the definition + of an empty sequence (it used to be [-1], because it was more alike + the original code in C++). + :return: empty action sequence + """ + return [] From ab275f2bfbef53deb8991c5915595f8ca9732eb5 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Thu, 20 Sep 2018 13:22:00 +0200 Subject: [PATCH 44/83] moved searching goal sequence to another class and added tests --- examples/acs2/handeye/utils.py | 15 +- lcs/agents/acs2/ClassifiersList.py | 248 +--------- lcs/strategies/action_planning.py | 473 +++++++++---------- tests/lcs/agents/acs2/test_Classifier.py | 41 ++ tests/lcs/strategies/test_action_planning.py | 296 ++++++++++++ 5 files changed, 589 insertions(+), 484 deletions(-) create mode 100644 tests/lcs/strategies/test_action_planning.py diff --git a/examples/acs2/handeye/utils.py b/examples/acs2/handeye/utils.py index 781cff31..1abc6f63 100644 --- a/examples/acs2/handeye/utils.py +++ b/examples/acs2/handeye/utils.py @@ -13,6 +13,10 @@ def calculate_performance(hand_eye, population): # Count how many transitions are anticipated correctly nr_correct = 0 + nr_with_block = 0 + nr_correct_with_block = 0 + + # note: knowledge with block only works if env has note_in_hand set to True # For all possible destinations from each path cell for start, action, end in transitions: @@ -20,10 +24,19 @@ def calculate_performance(hand_eye, population): p0 = start p1 = end + if p0[-1] == '2' or p1[-1] == '2': + nr_with_block += 1 + if any([True for cl in reliable_classifiers if cl.predicts_successfully(p0, action, p1)]): nr_correct += 1 + if p0[-1] == '2' or p1[-1] == '2': + nr_correct_with_block += 1 return { - 'knowledge': nr_correct / len(transitions) * 100.0 + 'knowledge': nr_correct / len(transitions) * 100.0, + 'with_block': nr_correct_with_block / nr_with_block * 100.0, + 'no_block': + (nr_correct - nr_correct_with_block) / + (len(transitions) - nr_with_block) * 100.0 } diff --git a/lcs/agents/acs2/ClassifiersList.py b/lcs/agents/acs2/ClassifiersList.py index 137bfeb3..d0a85fad 100644 --- a/lcs/agents/acs2/ClassifiersList.py +++ b/lcs/agents/acs2/ClassifiersList.py @@ -11,6 +11,7 @@ import mutate, two_point_crossover from lcs.strategies.genetic_algorithms import roulette_wheel_selection from . import Classifier, Configuration +from lcs.strategies.action_planning import GoalSequenceSearcher class ClassifiersList(TypedList): @@ -457,248 +458,5 @@ def search_goal_sequence(self, start, goal): get_quality_classifiers_list(quality=self.cfg.theta_r, cfg=self.cfg) - if len(reliable_classifiers) < 1: - return self._empty_sequence() - - self.forward_classifiers = [] - self.backward_classifiers = [] - - self.forward_perceptions = [] - self.backward_perceptions = [] - - max_depth = 6 - forward_size = 1 - backward_size = 1 - forward_point = 0 - backward_point = 0 - action_sequence = None - - self.forward_perceptions.append(Perception(start)) - self.backward_perceptions.append(Perception(goal)) - - for depth in range(0, max_depth): - # forward step - action_sequence, forward_size_new = self. \ - _search_one_forward_step(reliable_classifiers, forward_size, - forward_point) - forward_point = forward_size - forward_size = forward_size_new - - if action_sequence is not None: - return action_sequence - - # backwards step - action_sequence, backward_size_new = self. \ - _search_one_backward_step(reliable_classifiers, backward_size, - backward_point) - backward_point = backward_size - backward_size = backward_size_new - - if action_sequence is not None: - return action_sequence - - # depth limit was reached -> return empty action sequence - return self._empty_sequence() - - def _search_one_forward_step(self, reliable_classifiers, forward_size, - forward_point): - """ - Serches one step forward in the reliable_classifiers classifier list. - Returns None if nothing was found so far, a sequence with a -1 element - if the search failed completely - (which is the case if the allowed array size of 10000 is reached), - or the sequence if one was found. - :param reliable_classifiers: ClassifiersList - :param forward_size: int - :param forward_point: int - :return: act sequence and new forward_size - """ - size = forward_size - for i in range(forward_point, forward_size): - match_forward = reliable_classifiers. \ - form_match_set(situation=self.forward_perceptions[i], - cfg=self.cfg) - for match_set_element in match_forward: - anticipation = match_set_element. \ - get_best_anticipation(self.forward_perceptions[i]) - if self._does_contain_state(self.forward_perceptions, - anticipation) is None: - # state not detected forward -> search in backwards - backward_sequence_idx = self. \ - _does_contain_state(self.backward_perceptions, - anticipation) - if backward_sequence_idx is None: - # state neither detected backwards - self.forward_perceptions.append(anticipation) - self.forward_classifiers.append( - self._form_new_classifiers( - self.forward_classifiers, i, - match_set_element)) - size += 1 - if size > 10001: - # logging.debug("Arrays are full") - return self._empty_sequence(), size - else: - # sequence found - return self. \ - _sequence_found_forwards( - i, backward_sequence_idx, - match_set_element), size - return None, size - - def _search_one_backward_step(self, reliable_classifiers, backward_size, - backward_point): - """ - Searches one step backward in the reliable_classifiers classifiers list - Returns None if nothing was found so far, a sequence with a -1 element - if the search failed completely - (which is the case if the allowed array size of 10000 is reached), - or the sequence if one was found. - :param reliable_classifiers: ClassifiersList - :param backward_size: int - :param backward_point: int - :return: act sequence and new backward_size - """ - size = backward_size - for i in range(backward_point, backward_size): - match_backward = reliable_classifiers. \ - form_match_set_backwards( - situation=self.backward_perceptions[i], - cfg=self.cfg) - for match_set_el in match_backward: - anticipation = match_set_el. \ - get_backwards_anticipation(self.backward_perceptions[i]) - if anticipation is not None and \ - self._does_contain_state(self.backward_perceptions, - anticipation) is None: - # Backwards anticipation was formable but - # not detected backwards - forward_sequence_idx = self. \ - _does_contain_state(self.forward_perceptions, - anticipation) - if forward_sequence_idx is None: - self.backward_perceptions.append(anticipation) - self.backward_classifiers.append( - self._form_new_classifiers( - self.backward_classifiers, i, match_set_el)) - size += 1 - if size > 10001: - # logging.debug("Arrays are full") - return self._empty_sequence(), size - else: - return self. \ - _sequence_found_backwards( - i, forward_sequence_idx, - match_set_el), size - return None, size - - def _form_new_classifiers(self, classifiers_lists, i, match_set_el): - """ - Executes actions after sequence was not detected. - :param classifiers_lists: list of ClassifiersLists - :param i: int - :param match_set_el: Classifier - :return: new size of classifiers - """ - if i > 0: - new_classifiers = ClassifiersList(cfg=self.cfg) - new_classifiers.extend(classifiers_lists[i - 1]) - else: - new_classifiers = ClassifiersList(cfg=self.cfg) - new_classifiers.append(match_set_el) - return new_classifiers - - def _sequence_found_forwards(self, i, backward_sequence_idx, match_set_el): - """ - Forms sequence when it was found forwards. - :param i: - :param backward_sequence_idx: - :param match_set_el: Classifier - :return: act sequence - """ - # count sequence size - sequence_size = 0 - if i > 0: - sequence_size += len(self.forward_classifiers[i - 1]) - if backward_sequence_idx > 0: - sequence_size += len( - self.backward_classifiers[backward_sequence_idx - 1]) - sequence_size += 1 - - # construct sequence - act_seq = [-1] * sequence_size - j = 0 - if i > 0: - for j, cl in enumerate(self.forward_classifiers[i - 1]): - act_seq[len(self.forward_classifiers[i - 1]) - j - 1] \ - = cl.action - j += 1 - act_seq[j] = match_set_el.action - j += 1 - if backward_sequence_idx > 0: - for k, cl in enumerate( - self.backward_classifiers[backward_sequence_idx - 1]): - act_seq[k + j] = cl.action - return act_seq - - def _sequence_found_backwards(self, i, forward_sequence_idx, match_set_el): - """ - Forms sequence when it was found backwards. - :param i: int - :param forward_sequence_idx: int - :param match_set_el: Classifier - :return: act sequence - """ - # count sequence size - sequence_size = 0 - if i > 0: - sequence_size += len(self.backward_classifiers[i - 1]) - if forward_sequence_idx > 0: - sequence_size += len( - self.forward_classifiers[forward_sequence_idx - 1]) - sequence_size += 1 - - # construct sequence - act_seq = [-1] * sequence_size - j = 0 - if forward_sequence_idx > 0: - for j, cl in enumerate( - self.forward_classifiers[forward_sequence_idx - 1]): - act_seq[ - len(self. - forward_classifiers[ - forward_sequence_idx - 1]) - j - 1] \ - = cl.action - j += 1 - act_seq[j] = match_set_el.action - j += 1 - if i > 0: - for k, cl in enumerate(self.backward_classifiers[i - 1]): - act_seq[k + j] = cl.action - return act_seq - - @staticmethod - def _does_contain_state(perceptions, state): - """ - Returns the position in the perception list where 'state' is stored or - None if state is not found - :param perceptions: Perception list - :param state: Perception - :return: - """ - for i, percept in enumerate(perceptions): - if percept == state: - return i - return None - - @staticmethod - def _empty_sequence(): - """ - Returns empty sequence. - This function might be deleted in later versions of code, but it is - useful if we decide to change the definition - of an empty sequence (it used to be [-1], because it was more alike - the original code in C++). - :return: empty action sequence - """ - return [] + return GoalSequenceSearcher().search_goal_sequence( + reliable_classifiers, start, goal) diff --git a/lcs/strategies/action_planning.py b/lcs/strategies/action_planning.py index 4707b66f..10a39e5d 100644 --- a/lcs/strategies/action_planning.py +++ b/lcs/strategies/action_planning.py @@ -1,261 +1,258 @@ from lcs import Perception +from lcs.agents.acs2 import ClassifiersList -forward_classifiers = [] -backward_classifiers = [] -forward_perceptions = [] -backward_perceptions = [] +class GoalSequenceSearcher: + def __init__(self): + self.forward_classifiers = [] + self.backward_classifiers = [] + self.forward_perceptions = [] + self.backward_perceptions = [] -def search_goal_sequence(reliable_classifiers, start, goal): - """ - Searches a path from start to goal using a bidirectional method in the - environmental model (i.e. the list of reliable classifiers). - :param reliable_classifiers: list of reliable classifiers - :param start: Perception - :param goal: Perception - :return: Sequence of actions - """ - if len(reliable_classifiers) < 1: - return empty_sequence() + def search_goal_sequence(self, reliable_classifiers, start, goal): + """ + Searches a path from start to goal using a bidirectional method in the + environmental model (i.e. the list of reliable classifiers). + :param reliable_classifiers: list of reliable classifiers + :param start: Perception + :param goal: Perception + :return: Sequence of actions + """ + if len(reliable_classifiers) < 1: + return self.empty_sequence() - max_depth = 6 - forward_size = 1 - backward_size = 1 - forward_point = 0 - backward_point = 0 - action_sequence = None + max_depth = 6 + forward_size = 1 + backward_size = 1 + forward_point = 0 + backward_point = 0 + action_sequence = None - forward_perceptions.append(Perception(start)) - backward_perceptions.append(Perception(goal)) + self.forward_classifiers.clear() + self.backward_classifiers.clear() + self.forward_perceptions.clear() + self.backward_perceptions.clear() - for depth in range(0, max_depth): - # forward step - action_sequence, forward_size_new = self. \ - _search_one_forward_step(reliable_classifiers, forward_size, - forward_point) - forward_point = forward_size - forward_size = forward_size_new + self.forward_perceptions.append(Perception(start)) + self.backward_perceptions.append(Perception(goal)) - if action_sequence is not None: - return action_sequence + for depth in range(0, max_depth): + # forward step + action_sequence, forward_size_new = \ + self._search_one_forward_step(reliable_classifiers, + forward_size, forward_point) + forward_point = forward_size + forward_size = forward_size_new - # backwards step - action_sequence, backward_size_new = self. \ - _search_one_backward_step(reliable_classifiers, backward_size, - backward_point) - backward_point = backward_size - backward_size = backward_size_new + if action_sequence is not None: + return action_sequence - if action_sequence is not None: - return action_sequence + # backwards step + action_sequence, backward_size_new = \ + self._search_one_backward_step(reliable_classifiers, + backward_size, backward_point) + backward_point = backward_size + backward_size = backward_size_new - # depth limit was reached -> return empty action sequence - return self._empty_sequence() + if action_sequence is not None: + return action_sequence + # depth limit was reached -> return empty action sequence + return self.empty_sequence() -def _search_one_forward_step(self, reliable_classifiers, forward_size, - forward_point): - """ - Serches one step forward in the reliable_classifiers classifier list. - Returns None if nothing was found so far, a sequence with a -1 element - if the search failed completely - (which is the case if the allowed array size of 10000 is reached), - or the sequence if one was found. - :param reliable_classifiers: ClassifiersList - :param forward_size: int - :param forward_point: int - :return: act sequence and new forward_size - """ - size = forward_size - for i in range(forward_point, forward_size): - match_forward = reliable_classifiers. \ - form_match_set(situation=self.forward_perceptions[i], - cfg=self.cfg) - for match_set_element in match_forward: - anticipation = match_set_element. \ - get_best_anticipation(self.forward_perceptions[i]) - if self._does_contain_state(self.forward_perceptions, - anticipation) is None: - # state not detected forward -> search in backwards - backward_sequence_idx = self. \ - _does_contain_state(self.backward_perceptions, - anticipation) - if backward_sequence_idx is None: - # state neither detected backwards - self.forward_perceptions.append(anticipation) - self.forward_classifiers.append( - self._form_new_classifiers( - self.forward_classifiers, i, - match_set_element)) - size += 1 - if size > 10001: - # logging.debug("Arrays are full") - return self._empty_sequence(), size - else: - # sequence found - return self. \ - _sequence_found_forwards( - i, backward_sequence_idx, - match_set_element), size - return None, size + def _search_one_forward_step(self, reliable_classifiers, forward_size, + forward_point): + """ + Serches one step forward in the reliable_classifiers classifier list. + Returns None if nothing was found so far, a sequence with a -1 element + if the search failed completely + (which is the case if the allowed array size of 10000 is reached), + or the sequence if one was found. + :param reliable_classifiers: ClassifiersList + :param forward_size: int + :param forward_point: int + :return: act sequence and new forward_size + """ + size = forward_size + for i in range(forward_point, forward_size): + match_forward = reliable_classifiers. \ + form_match_set(situation=self.forward_perceptions[i], + cfg=reliable_classifiers.cfg) + for match_set_element in match_forward: + anticipation = match_set_element. \ + get_best_anticipation(self.forward_perceptions[i]) + if self.does_contain_state(self.forward_perceptions, + anticipation) is None: + # state not detected forward -> search in backwards + backward_sequence_idx = self. \ + does_contain_state(self.backward_perceptions, + anticipation) + if backward_sequence_idx is None: + # state neither detected backwards + self.forward_perceptions.append(anticipation) + self.forward_classifiers.append( + self._form_new_classifiers( + self.forward_classifiers, i, match_set_element, + reliable_classifiers.cfg)) + size += 1 + if size > 10001: + # logging.debug("Arrays are full") + return self.empty_sequence(), size + else: + # sequence found + return self._form_sequence_forwards( + i, backward_sequence_idx, match_set_element), size + return None, size + def _search_one_backward_step(self, reliable_classifiers, backward_size, + backward_point): + """ + Searches one step backward in the reliable_classifiers classifiers list + Returns None if nothing was found so far, a sequence with a -1 element + if the search failed completely + (which is the case if the allowed array size of 10000 is reached), + or the sequence if one was found. + :param reliable_classifiers: ClassifiersList + :param backward_size: int + :param backward_point: int + :return: act sequence and new backward_size + """ + size = backward_size + for i in range(backward_point, backward_size): + match_backward = reliable_classifiers.form_match_set_backwards( + situation=self.backward_perceptions[i], + cfg=reliable_classifiers.cfg) + for match_set_el in match_backward: + anticipation = match_set_el. \ + get_backwards_anticipation(self.backward_perceptions[i]) + if anticipation is not None and self. \ + does_contain_state(self.backward_perceptions, + anticipation) is None: + # Backwards anticipation was formable but + # not detected backwards + forward_sequence_idx = self.\ + does_contain_state(self.forward_perceptions, + anticipation) + if forward_sequence_idx is None: + self.backward_perceptions.append(anticipation) + self.backward_classifiers.append( + self._form_new_classifiers( + self.backward_classifiers, i, match_set_el, + reliable_classifiers.cfg)) + size += 1 + if size > 10001: + # logging.debug("Arrays are full") + return self.empty_sequence(), size + else: + return self._form_sequence_backwards( + i, forward_sequence_idx, match_set_el), size + return None, size -def _search_one_backward_step(self, reliable_classifiers, backward_size, - backward_point): - """ - Searches one step backward in the reliable_classifiers classifiers list - Returns None if nothing was found so far, a sequence with a -1 element - if the search failed completely - (which is the case if the allowed array size of 10000 is reached), - or the sequence if one was found. - :param reliable_classifiers: ClassifiersList - :param backward_size: int - :param backward_point: int - :return: act sequence and new backward_size - """ - size = backward_size - for i in range(backward_point, backward_size): - match_backward = reliable_classifiers. \ - form_match_set_backwards( - situation=self.backward_perceptions[i], - cfg=self.cfg) - for match_set_el in match_backward: - anticipation = match_set_el. \ - get_backwards_anticipation(self.backward_perceptions[i]) - if anticipation is not None and \ - self._does_contain_state(self.backward_perceptions, - anticipation) is None: - # Backwards anticipation was formable but - # not detected backwards - forward_sequence_idx = self. \ - _does_contain_state(self.forward_perceptions, - anticipation) - if forward_sequence_idx is None: - self.backward_perceptions.append(anticipation) - self.backward_classifiers.append( - self._form_new_classifiers( - self.backward_classifiers, i, match_set_el)) - size += 1 - if size > 10001: - # logging.debug("Arrays are full") - return self._empty_sequence(), size - else: - return self. \ - _sequence_found_backwards( - i, forward_sequence_idx, - match_set_el), size - return None, size + @staticmethod + def _form_new_classifiers(classifiers_lists, i, match_set_el, cfg): + """ + Executes actions after sequence was not detected. + :param classifiers_lists: list of ClassifiersLists + :param i: int + :param match_set_el: Classifier + :return: new size of classifiers + """ + if i > 0: + new_classifiers = ClassifiersList.ClassifiersList(cfg=cfg) + new_classifiers.extend(classifiers_lists[i - 1]) + else: + new_classifiers = ClassifiersList.ClassifiersList(cfg=cfg) + new_classifiers.append(match_set_el) + return new_classifiers + def _form_sequence_forwards(self, i, backward_sequence_idx, match_set_el): + """ + Forms sequence when it was found forwards. + :param i: + :param backward_sequence_idx: + :param match_set_el: Classifier + :return: act sequence + """ + # count sequence size + sequence_size = 0 + if i > 0: + sequence_size += len(self.forward_classifiers[i - 1]) + if backward_sequence_idx > 0: + sequence_size += len(self.backward_classifiers[ + backward_sequence_idx - 1]) + sequence_size += 1 -def _form_new_classifiers(self, classifiers_lists, i, match_set_el): - """ - Executes actions after sequence was not detected. - :param classifiers_lists: list of ClassifiersLists - :param i: int - :param match_set_el: Classifier - :return: new size of classifiers - """ - if i > 0: - new_classifiers = ClassifiersList(cfg=self.cfg) - new_classifiers.extend(classifiers_lists[i - 1]) - else: - new_classifiers = ClassifiersList(cfg=self.cfg) - new_classifiers.append(match_set_el) - return new_classifiers - - -def _sequence_found_forwards(self, i, backward_sequence_idx, match_set_el): - """ - Forms sequence when it was found forwards. - :param i: - :param backward_sequence_idx: - :param match_set_el: Classifier - :return: act sequence - """ - # count sequence size - sequence_size = 0 - if i > 0: - sequence_size += len(self.forward_classifiers[i - 1]) - if backward_sequence_idx > 0: - sequence_size += len( - self.backward_classifiers[backward_sequence_idx - 1]) - sequence_size += 1 - - # construct sequence - act_seq = [-1] * sequence_size - j = 0 - if i > 0: - for j, cl in enumerate(self.forward_classifiers[i - 1]): - act_seq[len(self.forward_classifiers[i - 1]) - j - 1] \ - = cl.action + # construct sequence + act_seq = [-1] * sequence_size + j = 0 + if i > 0: + for j, cl in enumerate(self.forward_classifiers[i - 1]): + act_seq[len(self.forward_classifiers[i - 1]) - j - 1] \ + = cl.action + j += 1 + act_seq[j] = match_set_el.action j += 1 - act_seq[j] = match_set_el.action - j += 1 - if backward_sequence_idx > 0: - for k, cl in enumerate( - self.backward_classifiers[backward_sequence_idx - 1]): - act_seq[k + j] = cl.action - return act_seq + if backward_sequence_idx > 0: + for k, cl in enumerate(self.backward_classifiers[ + backward_sequence_idx - 1]): + act_seq[k + j] = cl.action + return act_seq + def _form_sequence_backwards(self, i, forward_sequence_idx, match_set_el): + """ + Forms sequence when it was found backwards. + :param i: int + :param forward_sequence_idx: int + :param match_set_el: Classifier + :return: act sequence + """ + # count sequence size + sequence_size = 0 + if i > 0: + sequence_size += len(self.backward_classifiers[i - 1]) + if forward_sequence_idx > 0: + sequence_size += len( + self.forward_classifiers[forward_sequence_idx - 1]) + sequence_size += 1 -def _sequence_found_backwards(self, i, forward_sequence_idx, match_set_el): - """ - Forms sequence when it was found backwards. - :param i: int - :param forward_sequence_idx: int - :param match_set_el: Classifier - :return: act sequence - """ - # count sequence size - sequence_size = 0 - if i > 0: - sequence_size += len(self.backward_classifiers[i - 1]) - if forward_sequence_idx > 0: - sequence_size += len( - self.forward_classifiers[forward_sequence_idx - 1]) - sequence_size += 1 - - # construct sequence - act_seq = [-1] * sequence_size - j = 0 - if forward_sequence_idx > 0: - for j, cl in enumerate( - self.forward_classifiers[forward_sequence_idx - 1]): - act_seq[ - len(self. - forward_classifiers[ - forward_sequence_idx - 1]) - j - 1] \ - = cl.action + # construct sequence + act_seq = [-1] * sequence_size + j = 0 + if forward_sequence_idx > 0: + for j, cl in enumerate(self.forward_classifiers[ + forward_sequence_idx - 1]): + act_seq[len(self.forward_classifiers[ + forward_sequence_idx - 1]) - j - 1] = cl.action + j += 1 + act_seq[j] = match_set_el.action j += 1 - act_seq[j] = match_set_el.action - j += 1 - if i > 0: - for k, cl in enumerate(self.backward_classifiers[i - 1]): - act_seq[k + j] = cl.action - return act_seq - - -def _does_contain_state(perceptions, state): - """ - Returns the position in the perception list where 'state' is stored or - None if state is not found - :param perceptions: Perception list - :param state: Perception - :return: - """ - for i, percept in enumerate(perceptions): - if percept == state: - return i - return None + if i > 0: + for k, cl in enumerate(self.backward_classifiers[i - 1]): + act_seq[k + j] = cl.action + return act_seq + @staticmethod + def does_contain_state(perceptions, state): + """ + Returns the position in the perception list where 'state' is stored or + None if state is not found + :param perceptions: Perception list + :param state: Perception + :return: + """ + for i, percept in enumerate(perceptions): + if percept == state: + return i + return None -def empty_sequence(): - """ - Returns empty sequence. - This function might be deleted in later versions of code, but it is - useful if we decide to change the definition - of an empty sequence (it used to be [-1], because it was more alike - the original code in C++). - :return: empty action sequence - """ - return [] + @staticmethod + def empty_sequence(): + """ + Returns empty sequence. + This function might be deleted in later versions of code, but it is + useful if we decide to change the definition + of an empty sequence (it used to be [-1], because it was more alike + the original code in C++). + :return: empty action sequence + """ + return [] diff --git a/tests/lcs/agents/acs2/test_Classifier.py b/tests/lcs/agents/acs2/test_Classifier.py index d2c74e35..dd29b801 100644 --- a/tests/lcs/agents/acs2/test_Classifier.py +++ b/tests/lcs/agents/acs2/test_Classifier.py @@ -857,3 +857,44 @@ def test_get_backwards_anticipation(self, cfg): assert result0 == ['0', '0', '1', '1', '1', '1', '1', '0'] assert result1 is None assert result2 is None + + def test_get_best_anticipation(self, cfg): + # given + p0 = Perception(['1', '1', '0', '1', '1', '1', '1', '1']) + p1 = Perception(['1', '1', '1', '1', '1', '1', '1', '1']) + classifier = Classifier(effect='##0##0##', cfg=cfg) + + # when + result0 = classifier.get_best_anticipation(p0) + result1 = classifier.get_best_anticipation(p1) + + # then + assert result0 == ['1', '1', '0', '1', '1', '0', '1', '1'] + assert result1 == ['1', '1', '0', '1', '1', '0', '1', '1'] + + @pytest.mark.parametrize("_c, _p, _result", [ + ('########', '10011001', True), + ('1#######', '10011001', True), + ('0#######', '10011001', False), + ]) + def test_should_match_perception(self, _c, _p, _result): + # given + c = Condition(_c) + p = Perception(_p) + + # then + assert c.does_match(p) is _result + + @pytest.mark.parametrize("_c, _other, _result", [ + ('########', '10011001', True), + ('1#######', '10011001', True), + ('0#######', '10011001', False), + ('####0###', '#1O##O##', True), + ]) + def test_should_match_condition(self, _c, _other, _result): + # given + c = Condition(_c) + other = Condition(_other) + + # then + assert c.does_match(other) is _result diff --git a/tests/lcs/strategies/test_action_planning.py b/tests/lcs/strategies/test_action_planning.py new file mode 100644 index 00000000..9c82221b --- /dev/null +++ b/tests/lcs/strategies/test_action_planning.py @@ -0,0 +1,296 @@ +from random import randint + +import pytest + +from lcs import Perception +from lcs.agents.acs2 import Configuration, ClassifiersList, Classifier +from lcs.strategies.action_planning import GoalSequenceSearcher + + +class TestActionPlanning: + + @pytest.fixture + def cfg(self): + return Configuration(8, 8) + + def test_does_contain_state(self): + # given + s0 = Perception("11111111") + s1 = Perception("00000000") + + perceptions0 = [s0] + perceptions1 = [s1] + perceptions2 = [s1, s0] + + # when + result_0 = GoalSequenceSearcher.does_contain_state(perceptions0, s0) + result_none = GoalSequenceSearcher.does_contain_state(perceptions1, s0) + result_1 = GoalSequenceSearcher.does_contain_state(perceptions2, s0) + + # then + assert result_0 == 0 + assert result_1 == 1 + assert result_none is None + + def test_empty_sequence(self): + # given, when + seq = GoalSequenceSearcher.empty_sequence() + + # then + assert seq == [] + + def test_search_goal_sequence_1(self, cfg): + # given + gs = GoalSequenceSearcher() + start = Perception("11111111") + goal = Perception("11111110") + + empty_list = ClassifiersList(cfg=cfg) + + # when + result = gs.search_goal_sequence(empty_list, start=start, goal=goal) + + assert result == gs.empty_sequence() + + @pytest.mark.skip(reason="not implemented yet") + def test_search_goal_sequence(self): + pass + + def test_form_new_classifiers_1(self, cfg): + # given + gs = GoalSequenceSearcher() + cls_list = [ClassifiersList(cfg=cfg)] + cl = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + i = 0 + + # when + new_classifiers = gs._form_new_classifiers(cls_list, i, cl, cfg) + + # then + assert len(new_classifiers) == 1 + assert cl in new_classifiers + + def test_form_new_classifiers_2(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + cls_list = [ClassifiersList(cl0, cfg=cfg)] + i = 1 + + # when + new_classifiers = gs._form_new_classifiers(cls_list, i, cl1, cfg) + + # then + assert len(new_classifiers) == 2 + assert cl0 in new_classifiers + assert cl1 in new_classifiers + + def test_form_sequence_forwards_1(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + i = 0 + idx = 0 + + # when + seq = gs._form_sequence_forwards(i, idx, cl0) + + # then + assert len(seq) == 1 + assert cl0.action in seq + + def test_form_sequence_forwards_2(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + i = 1 + idx = 0 + + # when + seq = gs._form_sequence_forwards(i, idx, cl1) + + # then + assert len(seq) == 2 + assert cl0.action in seq + assert cl1.action in seq + assert seq == [cl0.action, cl1.action] + + def test_form_sequence_forwards_3(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + gs.backward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + i = 0 + idx = 1 + + # when + seq = gs._form_sequence_forwards(i, idx, cl1) + + # then + assert len(seq) == 2 + assert cl0.action in seq + assert cl1.action in seq + assert seq == [cl1.action, cl0.action] + + def test_form_sequence_forwards_4(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + cl2 = Classifier(condition="11111111", action=1, effect="0000000", + cfg=cfg) + gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + gs.backward_classifiers = [ClassifiersList(cl1, cl0, cfg=cfg)] + i = 1 + idx = 1 + + # when + seq = gs._form_sequence_forwards(i, idx, cl2) + + # then + assert len(seq) == 4 + assert cl0.action in seq + assert cl1.action in seq + assert cl2.action in seq + assert seq == [cl0.action, cl2.action, cl1.action, cl0.action] + + def test_form_sequence_forwards_5(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + cl2 = Classifier(condition="11111111", action=1, effect="0000000", + cfg=cfg) + gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg), + ClassifiersList(cl0, cl1, cfg=cfg)] + i = 2 + idx = 0 + + # when + seq = gs._form_sequence_forwards(i, idx, cl2) + + # then + assert len(seq) == 3 + assert cl0.action in seq + assert cl1.action in seq + assert cl2.action in seq + assert seq == [cl1.action, cl0.action, cl2.action] + + def test_form_sequence_backwards_1(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + i = 0 + idx = 0 + + # when + seq = gs._form_sequence_backwards(i, idx, cl0) + + # then + assert len(seq) == 1 + assert cl0.action in seq + + def test_form_sequence_backwards_2(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + gs.backward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + i = 1 + idx = 0 + + # when + seq = gs._form_sequence_backwards(i, idx, cl1) + + # then + assert len(seq) == 2 + assert cl0.action in seq + assert cl1.action in seq + assert seq == [cl1.action, cl0.action] + + def test_form_sequence_backwards_3(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + i = 0 + idx = 1 + + # when + seq = gs._form_sequence_backwards(i, idx, cl1) + + # then + assert len(seq) == 2 + assert cl0.action in seq + assert cl1.action in seq + assert seq == [cl0.action, cl1.action] + + def test_form_sequence_backwards_4(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + cl2 = Classifier(condition="11111111", action=1, effect="0000000", + cfg=cfg) + gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + gs.backward_classifiers = [ClassifiersList(cl1, cl0, cfg=cfg)] + i = 1 + idx = 1 + + # when + seq = gs._form_sequence_backwards(i, idx, cl2) + + # then + assert len(seq) == 4 + assert cl0.action in seq + assert cl1.action in seq + assert cl2.action in seq + assert seq == [cl0.action, cl2.action, cl1.action, cl0.action] + + def test_form_sequence_backwards_5(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + cl2 = Classifier(condition="11111111", action=1, effect="0000000", + cfg=cfg) + gs.backward_classifiers = [ClassifiersList(cl0, cfg=cfg), + ClassifiersList(cl0, cl1, cfg=cfg)] + i = 2 + idx = 0 + + # when + seq = gs._form_sequence_backwards(i, idx, cl2) + + # then + assert len(seq) == 3 + assert cl0.action in seq + assert cl1.action in seq + assert cl2.action in seq + assert seq == [cl2.action, cl0.action, cl1.action] From 10d66088f43302043cf7ee0db6618e826b1c6e78 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Thu, 20 Sep 2018 16:12:18 +0200 Subject: [PATCH 45/83] added script to plot results --- examples/acs2/handeye/handeye.pdf | Bin 0 -> 18660 bytes examples/acs2/handeye/handeye_ap.pdf | Bin 0 -> 18622 bytes examples/acs2/handeye/handeye_no_ap.pdf | Bin 0 -> 18730 bytes ...HandEye2_ap_2018-09-20 16:01:56.258596.pdf | Bin 0 -> 17706 bytes ...dEye2_no_ap_2018-09-20 16:01:56.258596.pdf | Bin 0 -> 17892 bytes ...HandEye3_ap_2018-09-20 16:04:37.462759.pdf | Bin 0 -> 18969 bytes ...dEye3_no_ap_2018-09-20 16:04:37.462759.pdf | Bin 0 -> 18778 bytes .../handeye_ap_2018-09-20 15:51:58.908574.pdf | Bin 0 -> 18578 bytes ...ndeye_no_ap_2018-09-20 15:51:58.908574.pdf | Bin 0 -> 18615 bytes examples/acs2/handeye/plot_handeye.py | 148 ++++++++++++++++++ 10 files changed, 148 insertions(+) create mode 100644 examples/acs2/handeye/handeye.pdf create mode 100644 examples/acs2/handeye/handeye_ap.pdf create mode 100644 examples/acs2/handeye/handeye_no_ap.pdf create mode 100644 examples/acs2/handeye/images/HandEye2_ap_2018-09-20 16:01:56.258596.pdf create mode 100644 examples/acs2/handeye/images/HandEye2_no_ap_2018-09-20 16:01:56.258596.pdf create mode 100644 examples/acs2/handeye/images/HandEye3_ap_2018-09-20 16:04:37.462759.pdf create mode 100644 examples/acs2/handeye/images/HandEye3_no_ap_2018-09-20 16:04:37.462759.pdf create mode 100644 examples/acs2/handeye/images/handeye_ap_2018-09-20 15:51:58.908574.pdf create mode 100644 examples/acs2/handeye/images/handeye_no_ap_2018-09-20 15:51:58.908574.pdf create mode 100644 examples/acs2/handeye/plot_handeye.py diff --git a/examples/acs2/handeye/handeye.pdf b/examples/acs2/handeye/handeye.pdf new file mode 100644 index 0000000000000000000000000000000000000000..531f5c2bb5cb0e6ffbb6dbac183738476c164148 GIT binary patch literal 18660 zcmb`v1yq#J_dl*QOLyx6QWDz>OQ&=rpoFxP#L@~#Ba+e}f(TM74N@ZAsiI(@AcE4M z2#Uz>Sj8!X6g@D+z|YMV3{kN3v~+g82$q%x*}B-^GK&3CK*`rrS>Myr(-w@} zFLcJ$#S@I!PwQBEdfH-Lz$n}!2%>{^wbr-w1e*ZuD4qf8xApY|LsTyTZOZ?O6@JC4 zU^6g8-`>*3!Q~b1{o~?(g7uFgOAx=jBi4Z5%KjepF=QqD+9Gn3Wfgxv{ 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zbOr;f@P0Y0B}e`m=zn(pS6>(~@Y!p>lpC}~@ z_ILllsIV{r^{ZbGVDcQa^M?^f{p#KmjQZ8F7oa@{ja~zg#6fBwcVLO7 zfthvC!2hfU_WAdF8Q8(^CddGJzYYC=zx)8PFaz%&s6b@kzl#B&;K0KE?*}6OD2Di5 z3@QZRqN568Ur0&)VT-YY`bEwENW_lspG+)=|H;I%?)PBioIL<@4N$0NXNg5@`#&I| zi^U%YlpOpB#{YUjVWsq6W#E?>-0hs53QC z7P6_puc~WgJGA5LwUdh0BQfuLjY#w?>sp4?>?YND1dMe zmxG}Y*tv!M``_45uz&Lav;kWcnb4h6-oS%=F3d;7on2So`3km&DnqW{PeDhxvdGx>0P!ayhgktYKEoA*#r?1{}^bwv^X@<3D=*nRw6P89VI572M`di+%mK#TwMi#}p( zXpw*Ai$?#$KVT<<{73Jh$bWSlD)Nu+!T^>0dmJzr;$J<531f{U_V0gs3Ws8i-(Pvi zf9ZpxpnuN~7{Hdlc?Fzl|1;kJ829fyM_^&xU;QG0CG4L(G}gErrVp4O|K=gECjM|a z;Ky PAW$d}7ng#TBIy4C(g41c literal 0 HcmV?d00001 diff --git a/examples/acs2/handeye/plot_handeye.py b/examples/acs2/handeye/plot_handeye.py new file mode 100644 index 00000000..12faa0c2 --- /dev/null +++ b/examples/acs2/handeye/plot_handeye.py @@ -0,0 +1,148 @@ +# Plot constants +import datetime + +import gym +import gym_handeye + +from lcs.agents.acs2 import ACS2, ClassifiersList, Configuration +import numpy as np +import pandas as pd +import matplotlib +import matplotlib.pyplot as plt + +from examples.acs2.handeye.utils import calculate_performance + +TITLE_TEXT_SIZE=24 +AXIS_TEXT_SIZE=18 +LEGEND_TEXT_SIZE=16 + + +def parse_metrics_to_df(explore_metrics, exploit_metrics): + def extract_details(row): + row['trial'] = row['agent']['trial'] + row['steps'] = row['agent']['steps'] + row['numerosity'] = row['agent']['numerosity'] + row['reliable'] = row['agent']['reliable'] + row['knowledge'] = row['performance']['knowledge'] + row['with_block'] = row['performance']['with_block'] + row['no_block'] = row['performance']['no_block'] + return row + + # Load both metrics into data frame + explore_df = pd.DataFrame(explore_metrics) + exploit_df = pd.DataFrame(exploit_metrics) + + # Mark them with specific phase + explore_df['phase'] = 'explore' + exploit_df['phase'] = 'exploit' + + # Extract details + explore_df = explore_df.apply(extract_details, axis=1) + exploit_df = exploit_df.apply(extract_details, axis=1) + + # Adjuts exploit trial counter + exploit_df['trial'] = exploit_df.apply( + lambda r: r['trial'] + len(explore_df), axis=1) + + # Concatenate both dataframes + df = pd.concat([explore_df, exploit_df]) + df.drop(['agent', 'environment', 'performance'], axis=1, inplace=True) + df.set_index('trial', inplace=True) + + return df + + +def plot_knowledge(df, ax=None): + if ax is None: + ax = plt.gca() + + explore_df = df.query("phase == 'explore'") + exploit_df = df.query("phase == 'exploit'") + + explore_df['knowledge'].plot(ax=ax, c='blue') + explore_df['with_block'].plot(ax=ax, c='green') + explore_df['no_block'].plot(ax=ax, c='yellow') + exploit_df['knowledge'].plot(ax=ax, c='red') + ax.axvline(x=len(explore_df), c='black', linestyle='dashed') + + ax.set_title("Achieved knowledge", fontsize=TITLE_TEXT_SIZE) + ax.set_xlabel("Trial", fontsize=AXIS_TEXT_SIZE) + ax.set_ylabel("Knowledge [%]", fontsize=AXIS_TEXT_SIZE) + ax.set_ylim([0, 105]) + ax.legend(fontsize=LEGEND_TEXT_SIZE) + +def plot_classifiers(df, ax=None): + if ax is None: + ax = plt.gca() + + explore_df = df.query("phase == 'explore'") + exploit_df = df.query("phase == 'exploit'") + + df['numerosity'].plot(ax=ax, c='blue') + df['reliable'].plot(ax=ax, c='red') + + ax.axvline(x=len(explore_df), c='black', linestyle='dashed') + + ax.set_title("Classifiers", fontsize=TITLE_TEXT_SIZE) + ax.set_xlabel("Trial", fontsize=AXIS_TEXT_SIZE) + ax.set_ylabel("Classifiers", fontsize=AXIS_TEXT_SIZE) + ax.legend(fontsize=LEGEND_TEXT_SIZE) + + +def plot_performance(agent, env, metrics_df, cfg, env_name, additional_info): + plt.figure(figsize=(13, 10), dpi=100) + plt.suptitle(f'ACS2 Performance in {env_name} environment ' + f'{additional_info}', fontsize=32) + + # ax1 = plt.subplot(221) + # plot_policy(env, agent, cfg, ax1) + + ax2 = plt.subplot(211) + plot_knowledge(metrics_df, ax2) + + ax3 = plt.subplot(212) + plot_classifiers(metrics_df, ax3) + + # ax4 = plt.subplot(224) + # plot_steps(metrics_df, ax4) + + plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3) + + +def plot_handeye(env_name='HandEye3', filename='images/handeye.pdf', do_action_planning=True): + hand_eye = gym.make('{}-v0'.format(env_name)) + cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, + epsilon=1.0, + do_ga=False, + do_action_planning=do_action_planning, + performance_fcn=calculate_performance) + + # explore + agent_he = ACS2(cfg) + population_maze5_explore, metrics_maze5_explore = agent_he.explore( + hand_eye, 50) + + # exploit + agent_he = ACS2(cfg, population_maze5_explore) + _, metrics_maze5_exploit = agent_he.exploit(hand_eye, 10) + + maze5_metrics_df = parse_metrics_to_df(metrics_maze5_explore, + metrics_maze5_exploit) + + if do_action_planning: + message = 'with' + else: + message = 'without' + + plot_performance(agent_he, hand_eye, maze5_metrics_df, cfg, env_name, + '\n{} Action Planning'.format(message)) + plt.savefig(filename, format='pdf', dpi=100) + + +if __name__ == "__main__": + date = datetime.datetime.now() + env_name = 'HandEye3' + plot_handeye(env_name, 'images/{}_ap_{}.pdf'.format(env_name, date), + do_action_planning=True) + plot_handeye(env_name, 'images/{}_no_ap_{}.pdf'.format(env_name, date), + do_action_planning=False) From a622a4706f03c7459ad9de50d3706bb7464441a8 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Fri, 21 Sep 2018 11:54:09 +0200 Subject: [PATCH 46/83] added some graphs --- ...dEye2-v0_ap_2018-09-21 11:25:11.446657.pdf | Bin 0 -> 17857 bytes ...e2-v0_no_ap_2018-09-21 11:25:11.446657.pdf | Bin 0 -> 18081 bytes ...dEye3-v0_ap_2018-09-21 11:27:24.223319.pdf | Bin 0 -> 18688 bytes ...dEye3-v0_ap_2018-09-21 11:41:38.637807.pdf | Bin 0 -> 18756 bytes ...e3-v0_no_ap_2018-09-21 11:27:24.223319.pdf | Bin 0 -> 18792 bytes ...e3-v0_no_ap_2018-09-21 11:41:38.637807.pdf | Bin 0 -> 19079 bytes ...dEye4-v0_ap_2018-09-21 09:04:07.609873.pdf | Bin 0 -> 18921 bytes ...e4-v0_no_ap_2018-09-21 09:04:07.609873.pdf | Bin 0 -> 18976 bytes ...dEye5-v0_ap_2018-09-21 09:18:25.359776.pdf | Bin 0 -> 18924 bytes ...dEye5-v0_ap_2018-09-21 09:50:43.258562.pdf | Bin 0 -> 18805 bytes ...e5-v0_no_ap_2018-09-21 09:50:43.258562.pdf | Bin 0 -> 18800 bytes ...dEye6-v0_ap_2018-09-21 10:19:16.162872.pdf | Bin 0 -> 18883 bytes ...e6-v0_no_ap_2018-09-21 10:19:16.162872.pdf | Bin 0 -> 18925 bytes examples/acs2/handeye/plot_handeye.py | 22 +++++++++++++----- lcs/agents/acs2/Condition.py | 2 +- 15 files changed, 17 insertions(+), 7 deletions(-) create mode 100644 examples/acs2/handeye/images/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf create mode 100644 examples/acs2/handeye/images/HandEye2-v0_no_ap_2018-09-21 11:25:11.446657.pdf create mode 100644 examples/acs2/handeye/images/HandEye3-v0_ap_2018-09-21 11:27:24.223319.pdf create mode 100644 examples/acs2/handeye/images/HandEye3-v0_ap_2018-09-21 11:41:38.637807.pdf create mode 100644 examples/acs2/handeye/images/HandEye3-v0_no_ap_2018-09-21 11:27:24.223319.pdf create mode 100644 examples/acs2/handeye/images/HandEye3-v0_no_ap_2018-09-21 11:41:38.637807.pdf create mode 100644 examples/acs2/handeye/images/HandEye4-v0_ap_2018-09-21 09:04:07.609873.pdf create mode 100644 examples/acs2/handeye/images/HandEye4-v0_no_ap_2018-09-21 09:04:07.609873.pdf create mode 100644 examples/acs2/handeye/images/HandEye5-v0_ap_2018-09-21 09:18:25.359776.pdf create mode 100644 examples/acs2/handeye/images/HandEye5-v0_ap_2018-09-21 09:50:43.258562.pdf create mode 100644 examples/acs2/handeye/images/HandEye5-v0_no_ap_2018-09-21 09:50:43.258562.pdf create mode 100644 examples/acs2/handeye/images/HandEye6-v0_ap_2018-09-21 10:19:16.162872.pdf create mode 100644 examples/acs2/handeye/images/HandEye6-v0_no_ap_2018-09-21 10:19:16.162872.pdf diff --git a/examples/acs2/handeye/images/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf b/examples/acs2/handeye/images/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf new file mode 100644 index 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zwM8MJhv^4J31N5KgJYo3*u{MiCV;^1eTQHI*gfhHOb{@D4#UvcJ^ElhK{$33J_JJ@ z0TaURa0lxNApnJU7zRxEAD9q!|2@zyH8c|HvO;6k#>M0i0pLspc@wz(xSv(KuWWSd;(2g#M9x7#t2*RfpQb1^&ek zE(jPmhwGsQ|Ct-$Ha;xlFoYoVA6yVtKo5`&lMMM=K}5L;*HgK;>#VIbrway|T8gw Date: Sat, 22 Sep 2018 15:33:17 +0200 Subject: [PATCH 47/83] added most required changes in code --- assets/ACS2/acs2++.cc | 4 ++ examples/acs2/handeye/utils.py | 14 ++++- lcs/Perception.py | 3 - lcs/agents/acs2/ACS2.py | 16 ++--- lcs/agents/acs2/ClassifiersList.py | 50 ---------------- lcs/agents/acs2/Condition.py | 6 +- lcs/agents/acs2/Effect.py | 4 +- lcs/strategies/action_planning/__init__.py | 0 .../action_planning/action_planning.py | 58 +++++++++++++++++++ .../goal_sequence_searcher.py} | 26 +++------ 10 files changed, 94 insertions(+), 87 deletions(-) create mode 100644 lcs/strategies/action_planning/__init__.py create mode 100644 lcs/strategies/action_planning/action_planning.py rename lcs/strategies/{action_planning.py => action_planning/goal_sequence_searcher.py} (92%) diff --git a/assets/ACS2/acs2++.cc b/assets/ACS2/acs2++.cc index 8077e030..9b2e5696 100755 --- a/assets/ACS2/acs2++.cc +++ b/assets/ACS2/acs2++.cc @@ -311,6 +311,10 @@ int startActionPlanning(ClassifierList *population, Environment *env, int time, } else { Action **actSequence = population->searchGoalSequence(situation, goalSituation); int i; + //cout<"; + //for(act=actSequence[0], i=0; act!=0; ++i, act=actSequence[i]) + // cout<"; + //cout< int: def __repr__(self): return ' '.join(map(str, self)) - def __setitem__(self, i, o): - self._items[i] = o - def __eq__(self, other): for si, oi in zip(self, other): if si != oi: diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index 21473f38..49bd8ad8 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -1,6 +1,7 @@ import logging from typing import Optional +from lcs.strategies.action_planning.action_planning import search_goal_sequence, exists_classifier from . import ClassifiersList, Configuration from ...agents import Agent from ...agents.Agent import Metric @@ -105,7 +106,7 @@ def _run_trial_explore(self, env, time, current_trial=None): while not done: if self.cfg.do_action_planning and \ - (steps + time) % self.cfg.action_planning_frequency == 0: + self._time_for_action_planning(steps + time): # Action Planning for increased model learning steps_ap, state, prev_state, action_set, reward = \ self._run_action_planning(env, steps + time, state, @@ -241,8 +242,8 @@ def _run_action_planning(self, env, time, situation, if goal_situation is None: break - act_sequence = self.population.search_goal_sequence(situation, - goal_situation) + act_sequence = search_goal_sequence(self.population, situation, + goal_situation) # Execute the found sequence and learn during executing i = 0 @@ -272,10 +273,8 @@ def _run_action_planning(self, env, time, situation, previous_situation = situation situation = parse_state(raw_state) - if not action_set.exists_classifier(previous_situation, - action, - situation, - self.cfg.theta_r): + if not exists_classifier(action_set, previous_situation, + action, situation, self.cfg.theta_r): # no reliable classifier was able to anticipate # such a change break @@ -288,6 +287,9 @@ def _run_action_planning(self, env, time, situation, return steps, situation, previous_situation, action_set, reward + def _time_for_action_planning(self, time): + return time % self.cfg.action_planning_frequency == 0 + def _collect_agent_metrics(self, trial, steps, total_steps) -> Metric: return { 'population': len(self.population), diff --git a/lcs/agents/acs2/ClassifiersList.py b/lcs/agents/acs2/ClassifiersList.py index d0a85fad..a92b38d9 100644 --- a/lcs/agents/acs2/ClassifiersList.py +++ b/lcs/agents/acs2/ClassifiersList.py @@ -11,7 +11,6 @@ import mutate, two_point_crossover from lcs.strategies.genetic_algorithms import roulette_wheel_selection from . import Classifier, Configuration -from lcs.strategies.action_planning import GoalSequenceSearcher class ClassifiersList(TypedList): @@ -411,52 +410,3 @@ def find_subsumer(self, cl: Classifier, choice_func=choice) -> \ return choice_func(most_general_subsumers) \ if most_general_subsumers else None - - def exists_classifier(self, previous_situation, action, situation, - quality): - """ - Returns True if there is a classifier in this list with a quality - higher than 'quality' that matches previous_situation, - specifies action, and predicts situation. - Returns False otherwise. - :param previous_situation: - :param action: - :param situation: - :param quality: - :return: - """ - for cl in self: - if cl.q > quality and cl.does_match(previous_situation) \ - and cl.action == action \ - and cl.does_anticipate_correctly(previous_situation, - situation): - return True - return False - - def get_quality_classifiers_list(self, quality, cfg=None): - """ - Constructs classifier list out of a list with q > quality. - :param quality: - :param cfg: - :return: ClassifiersList with only quality classifiers. - """ - listp = ClassifiersList(cfg=cfg) - for item in self: - if item.q > quality: - listp.append(item) - return listp - - def search_goal_sequence(self, start, goal): - """ - Searches a path from start to goal using a bidirectional method in the - environmental model (i.e. the list of reliable classifiers). - :param start: Perception - :param goal: Perception - :return: Sequence of actions - """ - reliable_classifiers = self. \ - get_quality_classifiers_list(quality=self.cfg.theta_r, - cfg=self.cfg) - - return GoalSequenceSearcher().search_goal_sequence( - reliable_classifiers, start, goal) diff --git a/lcs/agents/acs2/Condition.py b/lcs/agents/acs2/Condition.py index 605ba818..a9b35a4a 100644 --- a/lcs/agents/acs2/Condition.py +++ b/lcs/agents/acs2/Condition.py @@ -76,8 +76,8 @@ def get_backwards_anticipation(self, perception): :param perception: :return: """ - ant = Perception(perception) + ant = list(perception) for idx, item in enumerate(self): if item != self.wildcard: - ant[idx] = item # TODO - return ant + ant[idx] = item + return Perception(ant) diff --git a/lcs/agents/acs2/Effect.py b/lcs/agents/acs2/Effect.py index 1fc7738e..7a2e0b79 100644 --- a/lcs/agents/acs2/Effect.py +++ b/lcs/agents/acs2/Effect.py @@ -56,11 +56,11 @@ def get_best_anticipation(self, perception): """ # TODO: implement the rest after PEEs are implemented # ('getBestChar' function) - ant = Perception(perception) + ant = list(perception) for idx, item in enumerate(self): if item != self.wildcard: ant[idx] = item - return ant + return Perception(ant) def does_specify_only_changes_backwards(self, back_anticipation, situation): diff --git a/lcs/strategies/action_planning/__init__.py b/lcs/strategies/action_planning/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/lcs/strategies/action_planning/action_planning.py b/lcs/strategies/action_planning/action_planning.py new file mode 100644 index 00000000..0abc9bad --- /dev/null +++ b/lcs/strategies/action_planning/action_planning.py @@ -0,0 +1,58 @@ +from lcs.agents.acs2 import ClassifiersList +from lcs.strategies.action_planning.goal_sequence_searcher \ + import GoalSequenceSearcher + + +def exists_classifier(classifiers, previous_situation, action, situation, + quality): + """ + Returns True if there is a classifier in this list with a quality + higher than 'quality' that matches previous_situation, + specifies action, and predicts situation. + Returns False otherwise. + :param classifiers: + :param previous_situation: + :param action: + :param situation: + :param quality: + :return: + """ + for cl in classifiers: + if cl.q > quality and cl.does_match(previous_situation) \ + and cl.action == action \ + and cl.does_anticipate_correctly(previous_situation, + situation): + return True + return False + + +def get_quality_classifiers_list(classifiers, quality, cfg=None): + """ + Constructs classifier list out of a list with q > quality. + :param classifiers: + :param quality: + :param cfg: + :return: ClassifiersList with only quality classifiers. + """ + listp = ClassifiersList(cfg=cfg) + for item in classifiers: + if item.q > quality: + listp.append(item) + return listp + + +def search_goal_sequence(classifiers, start, goal): + """ + Searches a path from start to goal using a bidirectional method in the + environmental model (i.e. the list of reliable classifiers). + :param start: Perception + :param goal: Perception + :return: Sequence of actions + """ + reliable_classifiers = \ + get_quality_classifiers_list(classifiers, + quality=classifiers.cfg.theta_r, + cfg=classifiers.cfg) + + return GoalSequenceSearcher().search_goal_sequence( + reliable_classifiers, start, goal) diff --git a/lcs/strategies/action_planning.py b/lcs/strategies/action_planning/goal_sequence_searcher.py similarity index 92% rename from lcs/strategies/action_planning.py rename to lcs/strategies/action_planning/goal_sequence_searcher.py index 10a39e5d..18b764a9 100644 --- a/lcs/strategies/action_planning.py +++ b/lcs/strategies/action_planning/goal_sequence_searcher.py @@ -1,5 +1,5 @@ from lcs import Perception -from lcs.agents.acs2 import ClassifiersList +from lcs.agents.acs2.ClassifiersList import ClassifiersList class GoalSequenceSearcher: @@ -19,7 +19,7 @@ def search_goal_sequence(self, reliable_classifiers, start, goal): :return: Sequence of actions """ if len(reliable_classifiers) < 1: - return self.empty_sequence() + return [] max_depth = 6 forward_size = 1 @@ -58,7 +58,7 @@ def search_goal_sequence(self, reliable_classifiers, start, goal): return action_sequence # depth limit was reached -> return empty action sequence - return self.empty_sequence() + return [] def _search_one_forward_step(self, reliable_classifiers, forward_size, forward_point): @@ -97,7 +97,7 @@ def _search_one_forward_step(self, reliable_classifiers, forward_size, size += 1 if size > 10001: # logging.debug("Arrays are full") - return self.empty_sequence(), size + return [], size else: # sequence found return self._form_sequence_forwards( @@ -142,7 +142,7 @@ def _search_one_backward_step(self, reliable_classifiers, backward_size, size += 1 if size > 10001: # logging.debug("Arrays are full") - return self.empty_sequence(), size + return [], size else: return self._form_sequence_backwards( i, forward_sequence_idx, match_set_el), size @@ -158,10 +158,10 @@ def _form_new_classifiers(classifiers_lists, i, match_set_el, cfg): :return: new size of classifiers """ if i > 0: - new_classifiers = ClassifiersList.ClassifiersList(cfg=cfg) + new_classifiers = ClassifiersList(cfg=cfg) new_classifiers.extend(classifiers_lists[i - 1]) else: - new_classifiers = ClassifiersList.ClassifiersList(cfg=cfg) + new_classifiers = ClassifiersList(cfg=cfg) new_classifiers.append(match_set_el) return new_classifiers @@ -244,15 +244,3 @@ def does_contain_state(perceptions, state): if percept == state: return i return None - - @staticmethod - def empty_sequence(): - """ - Returns empty sequence. - This function might be deleted in later versions of code, but it is - useful if we decide to change the definition - of an empty sequence (it used to be [-1], because it was more alike - the original code in C++). - :return: empty action sequence - """ - return [] From be96cf4988d62674c5105b5ea4560fbd20dcf9ea Mon Sep 17 00:00:00 2001 From: e-dzia Date: Tue, 25 Sep 2018 09:25:36 +0200 Subject: [PATCH 48/83] something went wrong --- tests/lcs/agents/acs2/test_ClassifierList.py | 75 ----- tests/lcs/strategies/test_action_planning.py | 296 ------------------- 2 files changed, 371 deletions(-) delete mode 100644 tests/lcs/strategies/test_action_planning.py diff --git a/tests/lcs/agents/acs2/test_ClassifierList.py b/tests/lcs/agents/acs2/test_ClassifierList.py index 333942c0..fc0d3f30 100644 --- a/tests/lcs/agents/acs2/test_ClassifierList.py +++ b/tests/lcs/agents/acs2/test_ClassifierList.py @@ -1085,78 +1085,3 @@ def test_should_form_match_set_backwards(self, cfg): assert 2 == len(match_set) assert c1 in match_set assert c2 in match_set - - def test_get_quality_classifiers_list(self, cfg): - # given - population = ClassifiersList(cfg=cfg) - - # C1 - matching - c1 = Classifier(quality=0.9, cfg=cfg) - - # C2 - matching - c2 = Classifier(quality=0.7, cfg=cfg) - - # C3 - non-matching - c3 = Classifier(quality=0.5, cfg=cfg) - - # C4 - non-matching - c4 = Classifier(quality=0.1, cfg=cfg) - - population.append(c1) - population.append(c2) - population.append(c3) - population.append(c4) - - # when - match_set = population.get_quality_classifiers_list(0.5, cfg) - # then - assert 2 == len(match_set) - assert c1 in match_set - assert c2 in match_set - - def test_exists_classifier(self, cfg): - # given - population = ClassifiersList(cfg=cfg) - prev_situation = Perception('01100000') - situation = Perception('11110000') - act = 0 - q = 0.5 - - # C1 - OK - c1 = Classifier(condition='0##0####', action=0, effect='1##1####', - quality=0.7, cfg=cfg) - - # C2 - wrong action - c2 = Classifier(condition='0##0####', action=1, effect='1##1####', - quality=0.7, cfg=cfg) - - # C3 - wrong condition - c3 = Classifier(condition='0##1####', action=0, effect='1##1####', - quality=0.7, cfg=cfg) - - # C4 - wrong effect - c4 = Classifier(condition='0##0####', action=0, effect='1##0####', - quality=0.7, cfg=cfg) - - # C5 - wrong quality - c5 = Classifier(condition='0##0####', action=0, effect='1##1####', - quality=0.25, cfg=cfg) - - population.append(c2) - population.append(c3) - population.append(c4) - population.append(c5) - - # when - result0 = population.\ - exists_classifier(previous_situation=prev_situation, - situation=situation, action=act, quality=q) - - population.append(c1) - result1 = population.\ - exists_classifier(previous_situation=prev_situation, - situation=situation, action=act, quality=q) - - # then - assert result0 is False - assert result1 is True diff --git a/tests/lcs/strategies/test_action_planning.py b/tests/lcs/strategies/test_action_planning.py deleted file mode 100644 index 9c82221b..00000000 --- a/tests/lcs/strategies/test_action_planning.py +++ /dev/null @@ -1,296 +0,0 @@ -from random import randint - -import pytest - -from lcs import Perception -from lcs.agents.acs2 import Configuration, ClassifiersList, Classifier -from lcs.strategies.action_planning import GoalSequenceSearcher - - -class TestActionPlanning: - - @pytest.fixture - def cfg(self): - return Configuration(8, 8) - - def test_does_contain_state(self): - # given - s0 = Perception("11111111") - s1 = Perception("00000000") - - perceptions0 = [s0] - perceptions1 = [s1] - perceptions2 = [s1, s0] - - # when - result_0 = GoalSequenceSearcher.does_contain_state(perceptions0, s0) - result_none = GoalSequenceSearcher.does_contain_state(perceptions1, s0) - result_1 = GoalSequenceSearcher.does_contain_state(perceptions2, s0) - - # then - assert result_0 == 0 - assert result_1 == 1 - assert result_none is None - - def test_empty_sequence(self): - # given, when - seq = GoalSequenceSearcher.empty_sequence() - - # then - assert seq == [] - - def test_search_goal_sequence_1(self, cfg): - # given - gs = GoalSequenceSearcher() - start = Perception("11111111") - goal = Perception("11111110") - - empty_list = ClassifiersList(cfg=cfg) - - # when - result = gs.search_goal_sequence(empty_list, start=start, goal=goal) - - assert result == gs.empty_sequence() - - @pytest.mark.skip(reason="not implemented yet") - def test_search_goal_sequence(self): - pass - - def test_form_new_classifiers_1(self, cfg): - # given - gs = GoalSequenceSearcher() - cls_list = [ClassifiersList(cfg=cfg)] - cl = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - i = 0 - - # when - new_classifiers = gs._form_new_classifiers(cls_list, i, cl, cfg) - - # then - assert len(new_classifiers) == 1 - assert cl in new_classifiers - - def test_form_new_classifiers_2(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - cl1 = Classifier(condition="11111111", action=0, effect="0000000", - cfg=cfg) - cls_list = [ClassifiersList(cl0, cfg=cfg)] - i = 1 - - # when - new_classifiers = gs._form_new_classifiers(cls_list, i, cl1, cfg) - - # then - assert len(new_classifiers) == 2 - assert cl0 in new_classifiers - assert cl1 in new_classifiers - - def test_form_sequence_forwards_1(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - i = 0 - idx = 0 - - # when - seq = gs._form_sequence_forwards(i, idx, cl0) - - # then - assert len(seq) == 1 - assert cl0.action in seq - - def test_form_sequence_forwards_2(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - cl1 = Classifier(condition="11111111", action=0, effect="0000000", - cfg=cfg) - gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] - i = 1 - idx = 0 - - # when - seq = gs._form_sequence_forwards(i, idx, cl1) - - # then - assert len(seq) == 2 - assert cl0.action in seq - assert cl1.action in seq - assert seq == [cl0.action, cl1.action] - - def test_form_sequence_forwards_3(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - cl1 = Classifier(condition="11111111", action=0, effect="0000000", - cfg=cfg) - gs.backward_classifiers = [ClassifiersList(cl0, cfg=cfg)] - i = 0 - idx = 1 - - # when - seq = gs._form_sequence_forwards(i, idx, cl1) - - # then - assert len(seq) == 2 - assert cl0.action in seq - assert cl1.action in seq - assert seq == [cl1.action, cl0.action] - - def test_form_sequence_forwards_4(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - cl1 = Classifier(condition="11111111", action=0, effect="0000000", - cfg=cfg) - cl2 = Classifier(condition="11111111", action=1, effect="0000000", - cfg=cfg) - gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] - gs.backward_classifiers = [ClassifiersList(cl1, cl0, cfg=cfg)] - i = 1 - idx = 1 - - # when - seq = gs._form_sequence_forwards(i, idx, cl2) - - # then - assert len(seq) == 4 - assert cl0.action in seq - assert cl1.action in seq - assert cl2.action in seq - assert seq == [cl0.action, cl2.action, cl1.action, cl0.action] - - def test_form_sequence_forwards_5(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - cl1 = Classifier(condition="11111111", action=0, effect="0000000", - cfg=cfg) - cl2 = Classifier(condition="11111111", action=1, effect="0000000", - cfg=cfg) - gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg), - ClassifiersList(cl0, cl1, cfg=cfg)] - i = 2 - idx = 0 - - # when - seq = gs._form_sequence_forwards(i, idx, cl2) - - # then - assert len(seq) == 3 - assert cl0.action in seq - assert cl1.action in seq - assert cl2.action in seq - assert seq == [cl1.action, cl0.action, cl2.action] - - def test_form_sequence_backwards_1(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - i = 0 - idx = 0 - - # when - seq = gs._form_sequence_backwards(i, idx, cl0) - - # then - assert len(seq) == 1 - assert cl0.action in seq - - def test_form_sequence_backwards_2(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - cl1 = Classifier(condition="11111111", action=0, effect="0000000", - cfg=cfg) - gs.backward_classifiers = [ClassifiersList(cl0, cfg=cfg)] - i = 1 - idx = 0 - - # when - seq = gs._form_sequence_backwards(i, idx, cl1) - - # then - assert len(seq) == 2 - assert cl0.action in seq - assert cl1.action in seq - assert seq == [cl1.action, cl0.action] - - def test_form_sequence_backwards_3(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - cl1 = Classifier(condition="11111111", action=0, effect="0000000", - cfg=cfg) - gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] - i = 0 - idx = 1 - - # when - seq = gs._form_sequence_backwards(i, idx, cl1) - - # then - assert len(seq) == 2 - assert cl0.action in seq - assert cl1.action in seq - assert seq == [cl0.action, cl1.action] - - def test_form_sequence_backwards_4(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - cl1 = Classifier(condition="11111111", action=0, effect="0000000", - cfg=cfg) - cl2 = Classifier(condition="11111111", action=1, effect="0000000", - cfg=cfg) - gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] - gs.backward_classifiers = [ClassifiersList(cl1, cl0, cfg=cfg)] - i = 1 - idx = 1 - - # when - seq = gs._form_sequence_backwards(i, idx, cl2) - - # then - assert len(seq) == 4 - assert cl0.action in seq - assert cl1.action in seq - assert cl2.action in seq - assert seq == [cl0.action, cl2.action, cl1.action, cl0.action] - - def test_form_sequence_backwards_5(self, cfg): - # given - gs = GoalSequenceSearcher() - cl0 = Classifier(condition="01010101", action=2, effect="0000000", - cfg=cfg) - cl1 = Classifier(condition="11111111", action=0, effect="0000000", - cfg=cfg) - cl2 = Classifier(condition="11111111", action=1, effect="0000000", - cfg=cfg) - gs.backward_classifiers = [ClassifiersList(cl0, cfg=cfg), - ClassifiersList(cl0, cl1, cfg=cfg)] - i = 2 - idx = 0 - - # when - seq = gs._form_sequence_backwards(i, idx, cl2) - - # then - assert len(seq) == 3 - assert cl0.action in seq - assert cl1.action in seq - assert cl2.action in seq - assert seq == [cl2.action, cl0.action, cl1.action] From 5bb851af5e3362ac51877be677d90c2da72215f9 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Tue, 25 Sep 2018 09:26:53 +0200 Subject: [PATCH 49/83] Revert "something went wrong" This reverts commit be96cf4988d62674c5105b5ea4560fbd20dcf9ea. --- tests/lcs/agents/acs2/test_ClassifierList.py | 75 +++++ tests/lcs/strategies/test_action_planning.py | 296 +++++++++++++++++++ 2 files changed, 371 insertions(+) create mode 100644 tests/lcs/strategies/test_action_planning.py diff --git a/tests/lcs/agents/acs2/test_ClassifierList.py b/tests/lcs/agents/acs2/test_ClassifierList.py index fc0d3f30..333942c0 100644 --- a/tests/lcs/agents/acs2/test_ClassifierList.py +++ b/tests/lcs/agents/acs2/test_ClassifierList.py @@ -1085,3 +1085,78 @@ def test_should_form_match_set_backwards(self, cfg): assert 2 == len(match_set) assert c1 in match_set assert c2 in match_set + + def test_get_quality_classifiers_list(self, cfg): + # given + population = ClassifiersList(cfg=cfg) + + # C1 - matching + c1 = Classifier(quality=0.9, cfg=cfg) + + # C2 - matching + c2 = Classifier(quality=0.7, cfg=cfg) + + # C3 - non-matching + c3 = Classifier(quality=0.5, cfg=cfg) + + # C4 - non-matching + c4 = Classifier(quality=0.1, cfg=cfg) + + population.append(c1) + population.append(c2) + population.append(c3) + population.append(c4) + + # when + match_set = population.get_quality_classifiers_list(0.5, cfg) + # then + assert 2 == len(match_set) + assert c1 in match_set + assert c2 in match_set + + def test_exists_classifier(self, cfg): + # given + population = ClassifiersList(cfg=cfg) + prev_situation = Perception('01100000') + situation = Perception('11110000') + act = 0 + q = 0.5 + + # C1 - OK + c1 = Classifier(condition='0##0####', action=0, effect='1##1####', + quality=0.7, cfg=cfg) + + # C2 - wrong action + c2 = Classifier(condition='0##0####', action=1, effect='1##1####', + quality=0.7, cfg=cfg) + + # C3 - wrong condition + c3 = Classifier(condition='0##1####', action=0, effect='1##1####', + quality=0.7, cfg=cfg) + + # C4 - wrong effect + c4 = Classifier(condition='0##0####', action=0, effect='1##0####', + quality=0.7, cfg=cfg) + + # C5 - wrong quality + c5 = Classifier(condition='0##0####', action=0, effect='1##1####', + quality=0.25, cfg=cfg) + + population.append(c2) + population.append(c3) + population.append(c4) + population.append(c5) + + # when + result0 = population.\ + exists_classifier(previous_situation=prev_situation, + situation=situation, action=act, quality=q) + + population.append(c1) + result1 = population.\ + exists_classifier(previous_situation=prev_situation, + situation=situation, action=act, quality=q) + + # then + assert result0 is False + assert result1 is True diff --git a/tests/lcs/strategies/test_action_planning.py b/tests/lcs/strategies/test_action_planning.py new file mode 100644 index 00000000..9c82221b --- /dev/null +++ b/tests/lcs/strategies/test_action_planning.py @@ -0,0 +1,296 @@ +from random import randint + +import pytest + +from lcs import Perception +from lcs.agents.acs2 import Configuration, ClassifiersList, Classifier +from lcs.strategies.action_planning import GoalSequenceSearcher + + +class TestActionPlanning: + + @pytest.fixture + def cfg(self): + return Configuration(8, 8) + + def test_does_contain_state(self): + # given + s0 = Perception("11111111") + s1 = Perception("00000000") + + perceptions0 = [s0] + perceptions1 = [s1] + perceptions2 = [s1, s0] + + # when + result_0 = GoalSequenceSearcher.does_contain_state(perceptions0, s0) + result_none = GoalSequenceSearcher.does_contain_state(perceptions1, s0) + result_1 = GoalSequenceSearcher.does_contain_state(perceptions2, s0) + + # then + assert result_0 == 0 + assert result_1 == 1 + assert result_none is None + + def test_empty_sequence(self): + # given, when + seq = GoalSequenceSearcher.empty_sequence() + + # then + assert seq == [] + + def test_search_goal_sequence_1(self, cfg): + # given + gs = GoalSequenceSearcher() + start = Perception("11111111") + goal = Perception("11111110") + + empty_list = ClassifiersList(cfg=cfg) + + # when + result = gs.search_goal_sequence(empty_list, start=start, goal=goal) + + assert result == gs.empty_sequence() + + @pytest.mark.skip(reason="not implemented yet") + def test_search_goal_sequence(self): + pass + + def test_form_new_classifiers_1(self, cfg): + # given + gs = GoalSequenceSearcher() + cls_list = [ClassifiersList(cfg=cfg)] + cl = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + i = 0 + + # when + new_classifiers = gs._form_new_classifiers(cls_list, i, cl, cfg) + + # then + assert len(new_classifiers) == 1 + assert cl in new_classifiers + + def test_form_new_classifiers_2(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + cls_list = [ClassifiersList(cl0, cfg=cfg)] + i = 1 + + # when + new_classifiers = gs._form_new_classifiers(cls_list, i, cl1, cfg) + + # then + assert len(new_classifiers) == 2 + assert cl0 in new_classifiers + assert cl1 in new_classifiers + + def test_form_sequence_forwards_1(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + i = 0 + idx = 0 + + # when + seq = gs._form_sequence_forwards(i, idx, cl0) + + # then + assert len(seq) == 1 + assert cl0.action in seq + + def test_form_sequence_forwards_2(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + i = 1 + idx = 0 + + # when + seq = gs._form_sequence_forwards(i, idx, cl1) + + # then + assert len(seq) == 2 + assert cl0.action in seq + assert cl1.action in seq + assert seq == [cl0.action, cl1.action] + + def test_form_sequence_forwards_3(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + gs.backward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + i = 0 + idx = 1 + + # when + seq = gs._form_sequence_forwards(i, idx, cl1) + + # then + assert len(seq) == 2 + assert cl0.action in seq + assert cl1.action in seq + assert seq == [cl1.action, cl0.action] + + def test_form_sequence_forwards_4(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + cl2 = Classifier(condition="11111111", action=1, effect="0000000", + cfg=cfg) + gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + gs.backward_classifiers = [ClassifiersList(cl1, cl0, cfg=cfg)] + i = 1 + idx = 1 + + # when + seq = gs._form_sequence_forwards(i, idx, cl2) + + # then + assert len(seq) == 4 + assert cl0.action in seq + assert cl1.action in seq + assert cl2.action in seq + assert seq == [cl0.action, cl2.action, cl1.action, cl0.action] + + def test_form_sequence_forwards_5(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + cl2 = Classifier(condition="11111111", action=1, effect="0000000", + cfg=cfg) + gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg), + ClassifiersList(cl0, cl1, cfg=cfg)] + i = 2 + idx = 0 + + # when + seq = gs._form_sequence_forwards(i, idx, cl2) + + # then + assert len(seq) == 3 + assert cl0.action in seq + assert cl1.action in seq + assert cl2.action in seq + assert seq == [cl1.action, cl0.action, cl2.action] + + def test_form_sequence_backwards_1(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + i = 0 + idx = 0 + + # when + seq = gs._form_sequence_backwards(i, idx, cl0) + + # then + assert len(seq) == 1 + assert cl0.action in seq + + def test_form_sequence_backwards_2(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + gs.backward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + i = 1 + idx = 0 + + # when + seq = gs._form_sequence_backwards(i, idx, cl1) + + # then + assert len(seq) == 2 + assert cl0.action in seq + assert cl1.action in seq + assert seq == [cl1.action, cl0.action] + + def test_form_sequence_backwards_3(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + i = 0 + idx = 1 + + # when + seq = gs._form_sequence_backwards(i, idx, cl1) + + # then + assert len(seq) == 2 + assert cl0.action in seq + assert cl1.action in seq + assert seq == [cl0.action, cl1.action] + + def test_form_sequence_backwards_4(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + cl2 = Classifier(condition="11111111", action=1, effect="0000000", + cfg=cfg) + gs.forward_classifiers = [ClassifiersList(cl0, cfg=cfg)] + gs.backward_classifiers = [ClassifiersList(cl1, cl0, cfg=cfg)] + i = 1 + idx = 1 + + # when + seq = gs._form_sequence_backwards(i, idx, cl2) + + # then + assert len(seq) == 4 + assert cl0.action in seq + assert cl1.action in seq + assert cl2.action in seq + assert seq == [cl0.action, cl2.action, cl1.action, cl0.action] + + def test_form_sequence_backwards_5(self, cfg): + # given + gs = GoalSequenceSearcher() + cl0 = Classifier(condition="01010101", action=2, effect="0000000", + cfg=cfg) + cl1 = Classifier(condition="11111111", action=0, effect="0000000", + cfg=cfg) + cl2 = Classifier(condition="11111111", action=1, effect="0000000", + cfg=cfg) + gs.backward_classifiers = [ClassifiersList(cl0, cfg=cfg), + ClassifiersList(cl0, cl1, cfg=cfg)] + i = 2 + idx = 0 + + # when + seq = gs._form_sequence_backwards(i, idx, cl2) + + # then + assert len(seq) == 3 + assert cl0.action in seq + assert cl1.action in seq + assert cl2.action in seq + assert seq == [cl2.action, cl0.action, cl1.action] From d6cdb29aad5d13f4688a083d0b5de36cbc22757f Mon Sep 17 00:00:00 2001 From: e-dzia Date: Tue, 25 Sep 2018 11:16:44 +0200 Subject: [PATCH 50/83] fixed tests --- lcs/agents/acs2/ACS2.py | 3 +- tests/lcs/agents/acs2/test_Classifier.py | 70 ++++------ tests/lcs/agents/acs2/test_ClassifierList.py | 75 ---------- tests/lcs/agents/acs2/test_Effect.py | 129 +++++++----------- .../strategies/action_planning/__init__.py | 0 .../action_planning/test_action_planning.py | 91 ++++++++++++ .../test_goal_sequence_searcher.py} | 17 +-- 7 files changed, 174 insertions(+), 211 deletions(-) create mode 100644 tests/lcs/strategies/action_planning/__init__.py create mode 100644 tests/lcs/strategies/action_planning/test_action_planning.py rename tests/lcs/strategies/{test_action_planning.py => action_planning/test_goal_sequence_searcher.py} (96%) diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index 49bd8ad8..756bca1b 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -1,7 +1,8 @@ import logging from typing import Optional -from lcs.strategies.action_planning.action_planning import search_goal_sequence, exists_classifier +from lcs.strategies.action_planning.action_planning import \ + search_goal_sequence, exists_classifier from . import ClassifiersList, Configuration from ...agents import Agent from ...agents.Agent import Metric diff --git a/tests/lcs/agents/acs2/test_Classifier.py b/tests/lcs/agents/acs2/test_Classifier.py index dd29b801..24910c15 100644 --- a/tests/lcs/agents/acs2/test_Classifier.py +++ b/tests/lcs/agents/acs2/test_Classifier.py @@ -803,74 +803,54 @@ def test_should_generalize_second_unchanging_attribute(self, cfg): assert len(cls.specified_unchanging_attributes) == 1 assert Condition('#####0##') == cls.condition - def test_does_match_backwards(self, cfg): + @pytest.mark.parametrize("_e, _result", [ + ('#####1#1', True), + ('0######1', False), + ('#####0#1', False) + ]) + def test_does_match_backwards(self, cfg, _e, _result): # given percept = Perception('11111111') - - # C1 - matching - c1 = Classifier( - condition='#####0#0', - effect='#####1#1', - cfg=cfg) - - # C2 - non-matching - c2 = Classifier( - condition='#####0#0', - effect='0######1', - cfg=cfg) - - # C3 - non-matching - c3 = Classifier( - condition='#####0#0', - effect='#####0#1', - cfg=cfg) + c1 = Classifier(condition='#####0#0', effect=_e, cfg=cfg) # when result1 = c1.does_match_backwards(percept) - result2 = c2.does_match_backwards(percept) - result3 = c3.does_match_backwards(percept) # then - assert result1 is True - assert result2 is False - assert result3 is False + assert result1 is _result - def test_get_backwards_anticipation(self, cfg): + @pytest.mark.parametrize("_c, _e, _result", [ + ('#0###1##', '#1###0##', ['0', '0', '1', '1', '1', '1', '1', '0']), + ('#0###1##', '#0###0##', None), + ('#0###1##', '0####0##', None) + ]) + def test_get_backwards_anticipation(self, cfg, _c, _e, _result): # given p0 = Perception(['0', '1', '1', '1', '1', '0', '1', '0']) - - # C0 - OK - c0 = Classifier(condition='#0###1##', effect='#1###0##', cfg=cfg) - - # C1 - not OK - c1 = Classifier(condition='#0###1##', effect='#0###0##', cfg=cfg) - - # C1 - not OK - c2 = Classifier(condition='#0###1##', effect='0####0##', cfg=cfg) + c0 = Classifier(condition=_c, effect=_e, cfg=cfg) # when result0 = c0.get_backwards_anticipation(p0) - result1 = c1.get_backwards_anticipation(p0) - result2 = c2.get_backwards_anticipation(p0) # then - assert result0 == ['0', '0', '1', '1', '1', '1', '1', '0'] - assert result1 is None - assert result2 is None + assert result0 == _result - def test_get_best_anticipation(self, cfg): + @pytest.mark.parametrize("_p0, _result", [ + (['1', '1', '0', '1', '1', '1', '1', '1'], + ['1', '1', '0', '1', '1', '0', '1', '1']), + (['1', '1', '1', '1', '1', '1', '1', '1'], + ['1', '1', '0', '1', '1', '0', '1', '1']) + ]) + def test_get_best_anticipation(self, cfg, _p0, _result): # given - p0 = Perception(['1', '1', '0', '1', '1', '1', '1', '1']) - p1 = Perception(['1', '1', '1', '1', '1', '1', '1', '1']) + p0 = Perception(_p0) classifier = Classifier(effect='##0##0##', cfg=cfg) # when result0 = classifier.get_best_anticipation(p0) - result1 = classifier.get_best_anticipation(p1) # then - assert result0 == ['1', '1', '0', '1', '1', '0', '1', '1'] - assert result1 == ['1', '1', '0', '1', '1', '0', '1', '1'] + assert result0 == _result @pytest.mark.parametrize("_c, _p, _result", [ ('########', '10011001', True), diff --git a/tests/lcs/agents/acs2/test_ClassifierList.py b/tests/lcs/agents/acs2/test_ClassifierList.py index 333942c0..fc0d3f30 100644 --- a/tests/lcs/agents/acs2/test_ClassifierList.py +++ b/tests/lcs/agents/acs2/test_ClassifierList.py @@ -1085,78 +1085,3 @@ def test_should_form_match_set_backwards(self, cfg): assert 2 == len(match_set) assert c1 in match_set assert c2 in match_set - - def test_get_quality_classifiers_list(self, cfg): - # given - population = ClassifiersList(cfg=cfg) - - # C1 - matching - c1 = Classifier(quality=0.9, cfg=cfg) - - # C2 - matching - c2 = Classifier(quality=0.7, cfg=cfg) - - # C3 - non-matching - c3 = Classifier(quality=0.5, cfg=cfg) - - # C4 - non-matching - c4 = Classifier(quality=0.1, cfg=cfg) - - population.append(c1) - population.append(c2) - population.append(c3) - population.append(c4) - - # when - match_set = population.get_quality_classifiers_list(0.5, cfg) - # then - assert 2 == len(match_set) - assert c1 in match_set - assert c2 in match_set - - def test_exists_classifier(self, cfg): - # given - population = ClassifiersList(cfg=cfg) - prev_situation = Perception('01100000') - situation = Perception('11110000') - act = 0 - q = 0.5 - - # C1 - OK - c1 = Classifier(condition='0##0####', action=0, effect='1##1####', - quality=0.7, cfg=cfg) - - # C2 - wrong action - c2 = Classifier(condition='0##0####', action=1, effect='1##1####', - quality=0.7, cfg=cfg) - - # C3 - wrong condition - c3 = Classifier(condition='0##1####', action=0, effect='1##1####', - quality=0.7, cfg=cfg) - - # C4 - wrong effect - c4 = Classifier(condition='0##0####', action=0, effect='1##0####', - quality=0.7, cfg=cfg) - - # C5 - wrong quality - c5 = Classifier(condition='0##0####', action=0, effect='1##1####', - quality=0.25, cfg=cfg) - - population.append(c2) - population.append(c3) - population.append(c4) - population.append(c5) - - # when - result0 = population.\ - exists_classifier(previous_situation=prev_situation, - situation=situation, action=act, quality=q) - - population.append(c1) - result1 = population.\ - exists_classifier(previous_situation=prev_situation, - situation=situation, action=act, quality=q) - - # then - assert result0 is False - assert result1 is True diff --git a/tests/lcs/agents/acs2/test_Effect.py b/tests/lcs/agents/acs2/test_Effect.py index 671e492f..d09057f8 100644 --- a/tests/lcs/agents/acs2/test_Effect.py +++ b/tests/lcs/agents/acs2/test_Effect.py @@ -116,100 +116,75 @@ def test_eq(self): assert Effect('00001111') == Effect('00001111') assert Effect('00001111') != Effect('0000111#') - def test_does_match(self): - # given - p0 = Perception(['1', '1', '0', '1', '1', '1', '0', '1']) - p1 = Perception(['1', '1', '1', '1', '1', '0', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) - - # when - result = effect.does_match(p0, p1) - - # then - assert result is True - - def test_does_match_false(self): - # given - p0 = Perception(['1', '0', '1', '1', '1', '0', '0', '1']) - p1 = Perception(['1', '1', '0', '1', '1', '1', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) - - # when - result = effect.does_match(p0, p1) - - # then - assert result is False - - def test_does_match_false_2(self): - # given - p0 = Perception(['1', '1', '1', '1', '0', '0', '0', '1']) - p1 = Perception(['1', '1', '0', '1', '1', '1', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) - - # when - result = effect.does_match(p0, p1) - - # then - assert result is False - - def test_does_match_false_3(self): + @pytest.mark.parametrize("_p0, _p1, _e, _result", [ + (['1', '1', '0', '1', '1', '1', '0', '1'], + ['1', '1', '1', '1', '1', '0', '0', '1'], + ['#', '#', '0', '#', '#', '1', '#', '#'], + True), + (['1', '0', '1', '1', '1', '0', '0', '1'], + ['1', '1', '0', '1', '1', '1', '0', '1'], + ['#', '#', '0', '#', '#', '1', '#', '#'], + False), + (['1', '1', '1', '1', '0', '0', '0', '1'], + ['1', '1', '0', '1', '1', '1', '0', '1'], + ['#', '#', '0', '#', '#', '1', '#', '#'], + False), + (['1', '1', '1', '1', '1', '0', '1', '1'], + ['1', '1', '1', '1', '1', '1', '0', '1'], + ['#', '#', '0', '#', '#', '1', '#', '#'], + False) + ]) + def test_does_match(self, _p0, _p1, _e, _result): # given - p0 = Perception(['1', '1', '1', '1', '1', '0', '1', '1']) - p1 = Perception(['1', '1', '1', '1', '1', '1', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) + p0 = Perception(_p0) + p1 = Perception(_p1) + effect = Effect(_e) # when result = effect.does_match(p0, p1) # then - assert result is False + assert result is _result - def test_get_best_anticipation(self): + @pytest.mark.parametrize("_p0, _result", [ + (['1', '1', '0', '1', '1', '1', '1', '1'], + ['1', '1', '0', '1', '1', '0', '1', '1']), + (['1', '1', '1', '1', '1', '1', '1', '1'], + ['1', '1', '0', '1', '1', '0', '1', '1']) + ]) + def test_get_best_anticipation(self, _p0, _result): # given - p0 = Perception(['1', '1', '0', '1', '1', '1', '1', '1']) - p1 = Perception(['1', '1', '1', '1', '1', '1', '1', '1']) + p0 = Perception(_p0) effect = Effect(['#', '#', '0', '#', '#', '0', '#', '#']) # when result0 = effect.get_best_anticipation(p0) - result1 = effect.get_best_anticipation(p1) - - # then - assert result0 == ['1', '1', '0', '1', '1', '0', '1', '1'] - assert result1 == ['1', '1', '0', '1', '1', '0', '1', '1'] - - def test_does_specify_only_changes_backwards(self): - # given - back_ant = Perception(['1', '1', '0', '1', '1', '1', '1', '0']) - sit = Perception(['1', '1', '1', '1', '1', '1', '1', '0']) - effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#']) - - # when - result = effect.does_specify_only_changes_backwards(back_ant, sit) # then - assert result is True - - def test_does_specify_only_changes_backwards_false(self): - # given - back_ant = Perception(['1', '1', '0', '1', '1', '1', '1', '0']) - sit = Perception(['1', '1', '1', '1', '1', '1', '0', '0']) - effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#']) - - # when - result = effect.does_specify_only_changes_backwards(back_ant, sit) - - # then - assert result is False - - def test_does_specify_only_changes_backwards_false_2(self): + assert result0 == _result + + @pytest.mark.parametrize("_p0, _p1, _e, _result", [ + (['1', '1', '0', '1', '1', '1', '1', '0'], + ['1', '1', '1', '1', '1', '1', '1', '0'], + ['#', '#', '1', '#', '#', '0', '#', '#'], + True), + (['1', '1', '0', '1', '1', '1', '1', '0'], + ['1', '1', '1', '1', '1', '1', '0', '0'], + ['#', '#', '1', '#', '#', '0', '#', '#'], + False), + (['1', '1', '0', '1', '1', '0', '1', '0'], + ['1', '1', '1', '1', '1', '1', '1', '0'], + ['#', '#', '1', '#', '#', '0', '#', '#'], + False) + ]) + def test_does_specify_only_changes_backwards(self, _p0, _p1, _e, _result): # given - back_ant = Perception(['1', '1', '0', '1', '1', '0', '1', '0']) - sit = Perception(['1', '1', '1', '1', '1', '1', '1', '0']) - effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#']) + back_ant = Perception(_p0) + sit = Perception(_p1) + effect = Effect(_e) # when result = effect.does_specify_only_changes_backwards(back_ant, sit) # then - assert result is False + assert result is _result diff --git a/tests/lcs/strategies/action_planning/__init__.py b/tests/lcs/strategies/action_planning/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/lcs/strategies/action_planning/test_action_planning.py b/tests/lcs/strategies/action_planning/test_action_planning.py new file mode 100644 index 00000000..8a73c1db --- /dev/null +++ b/tests/lcs/strategies/action_planning/test_action_planning.py @@ -0,0 +1,91 @@ +from random import randint + +import pytest + +from lcs import Perception +from lcs.agents.acs2 import Configuration, ClassifiersList, Classifier +from lcs.strategies.action_planning.action_planning import \ + get_quality_classifiers_list, exists_classifier + + +class TestActionPlanning: + + @pytest.fixture + def cfg(self): + return Configuration(8, 8) + + def test_get_quality_classifiers_list(self, cfg): + # given + population = ClassifiersList(cfg=cfg) + + # C1 - matching + c1 = Classifier(quality=0.9, cfg=cfg) + + # C2 - matching + c2 = Classifier(quality=0.7, cfg=cfg) + + # C3 - non-matching + c3 = Classifier(quality=0.5, cfg=cfg) + + # C4 - non-matching + c4 = Classifier(quality=0.1, cfg=cfg) + + population.append(c1) + population.append(c2) + population.append(c3) + population.append(c4) + + # when + match_set = get_quality_classifiers_list(population, 0.5, cfg) + + # then + assert 2 == len(match_set) + assert c1 in match_set + assert c2 in match_set + + def test_exists_classifier(self, cfg): + # given + population = ClassifiersList(cfg=cfg) + prev_situation = Perception('01100000') + situation = Perception('11110000') + act = 0 + q = 0.5 + + # C1 - OK + c1 = Classifier(condition='0##0####', action=0, effect='1##1####', + quality=0.7, cfg=cfg) + + # C2 - wrong action + c2 = Classifier(condition='0##0####', action=1, effect='1##1####', + quality=0.7, cfg=cfg) + + # C3 - wrong condition + c3 = Classifier(condition='0##1####', action=0, effect='1##1####', + quality=0.7, cfg=cfg) + + # C4 - wrong effect + c4 = Classifier(condition='0##0####', action=0, effect='1##0####', + quality=0.7, cfg=cfg) + + # C5 - wrong quality + c5 = Classifier(condition='0##0####', action=0, effect='1##1####', + quality=0.25, cfg=cfg) + + population.append(c2) + population.append(c3) + population.append(c4) + population.append(c5) + + # when + result0 = exists_classifier(population, + previous_situation=prev_situation, + situation=situation, action=act, quality=q) + + population.append(c1) + result1 = exists_classifier(population, + previous_situation=prev_situation, + situation=situation, action=act, quality=q) + + # then + assert result0 is False + assert result1 is True diff --git a/tests/lcs/strategies/test_action_planning.py b/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py similarity index 96% rename from tests/lcs/strategies/test_action_planning.py rename to tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py index 9c82221b..0c614ca1 100644 --- a/tests/lcs/strategies/test_action_planning.py +++ b/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py @@ -1,14 +1,12 @@ -from random import randint - import pytest from lcs import Perception from lcs.agents.acs2 import Configuration, ClassifiersList, Classifier -from lcs.strategies.action_planning import GoalSequenceSearcher - +from lcs.strategies.action_planning.goal_sequence_searcher \ + import GoalSequenceSearcher -class TestActionPlanning: +class TestGoalSequenceSearcher: @pytest.fixture def cfg(self): return Configuration(8, 8) @@ -32,13 +30,6 @@ def test_does_contain_state(self): assert result_1 == 1 assert result_none is None - def test_empty_sequence(self): - # given, when - seq = GoalSequenceSearcher.empty_sequence() - - # then - assert seq == [] - def test_search_goal_sequence_1(self, cfg): # given gs = GoalSequenceSearcher() @@ -50,7 +41,7 @@ def test_search_goal_sequence_1(self, cfg): # when result = gs.search_goal_sequence(empty_list, start=start, goal=goal) - assert result == gs.empty_sequence() + assert result == [] @pytest.mark.skip(reason="not implemented yet") def test_search_goal_sequence(self): From 9491017357264126d681c54e59365f45835a426c Mon Sep 17 00:00:00 2001 From: e-dzia Date: Tue, 25 Sep 2018 11:31:05 +0200 Subject: [PATCH 51/83] added type hinting in action_planning.py --- .../action_planning/action_planning.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/lcs/strategies/action_planning/action_planning.py b/lcs/strategies/action_planning/action_planning.py index 0abc9bad..3e13ed26 100644 --- a/lcs/strategies/action_planning/action_planning.py +++ b/lcs/strategies/action_planning/action_planning.py @@ -1,10 +1,14 @@ -from lcs.agents.acs2 import ClassifiersList +from lcs import Perception +from lcs.agents.acs2 import ClassifiersList, Configuration from lcs.strategies.action_planning.goal_sequence_searcher \ import GoalSequenceSearcher -def exists_classifier(classifiers, previous_situation, action, situation, - quality): +def exists_classifier(classifiers: ClassifiersList, + previous_situation: Perception, + action: int, + situation: Perception, + quality: float): """ Returns True if there is a classifier in this list with a quality higher than 'quality' that matches previous_situation, @@ -26,7 +30,9 @@ def exists_classifier(classifiers, previous_situation, action, situation, return False -def get_quality_classifiers_list(classifiers, quality, cfg=None): +def get_quality_classifiers_list(classifiers: ClassifiersList, + quality: float, + cfg: Configuration = None): """ Constructs classifier list out of a list with q > quality. :param classifiers: @@ -41,10 +47,13 @@ def get_quality_classifiers_list(classifiers, quality, cfg=None): return listp -def search_goal_sequence(classifiers, start, goal): +def search_goal_sequence(classifiers: ClassifiersList, + start: Perception, + goal: Perception): """ Searches a path from start to goal using a bidirectional method in the environmental model (i.e. the list of reliable classifiers). + :param classifiers: :param start: Perception :param goal: Perception :return: Sequence of actions From 022d976ce286be42deb6e1c20dd1509e8a779172 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Tue, 25 Sep 2018 11:32:31 +0200 Subject: [PATCH 52/83] added type hinting in Classifier.py --- lcs/agents/acs2/Classifier.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/lcs/agents/acs2/Classifier.py b/lcs/agents/acs2/Classifier.py index 9d7704ce..ca569257 100644 --- a/lcs/agents/acs2/Classifier.py +++ b/lcs/agents/acs2/Classifier.py @@ -397,7 +397,7 @@ def does_subsume(self, other: Classifier) -> bool: def is_marked(self): return self.mark.is_marked() - def does_match(self, situation): + def does_match(self, situation: Perception): """ Returns if the classifier matches the situation. :param situation: @@ -405,7 +405,7 @@ def does_match(self, situation): """ return self.condition.does_match(situation) - def does_match_backwards(self, situation): + def does_match_backwards(self, situation: Perception): """ Returns if 'situation' is matched by the anticipations. This is only the case if the specified conditions that have #-symbols @@ -418,7 +418,7 @@ def does_match_backwards(self, situation): return True return False - def get_best_anticipation(self, perception): + def get_best_anticipation(self, perception: Perception): """ Returns the anticipation, the classifier believes to happen most probably. This is usually the normal anticipation. @@ -429,7 +429,7 @@ def get_best_anticipation(self, perception): """ return self.effect.get_best_anticipation(perception) - def get_backwards_anticipation(self, perception): + def get_backwards_anticipation(self, perception: Perception): """ Returns the backwards anticipation. Returns -1 if the backwards anticipation was impossible to create. From 286027452ddf3909fbca9a1011904b4125437fb6 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Tue, 25 Sep 2018 11:34:02 +0200 Subject: [PATCH 53/83] added type hinting in agents/acs2 --- lcs/agents/acs2/Condition.py | 2 +- lcs/agents/acs2/Effect.py | 9 +++++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/lcs/agents/acs2/Condition.py b/lcs/agents/acs2/Condition.py index a9b35a4a..79311da6 100644 --- a/lcs/agents/acs2/Condition.py +++ b/lcs/agents/acs2/Condition.py @@ -69,7 +69,7 @@ def does_match(self, other: Union[Perception, Condition]) -> bool: return True - def get_backwards_anticipation(self, perception): + def get_backwards_anticipation(self, perception: Perception): """ Returns the believed backwards anticipation. Hereby, the condition is treated like an effect part. diff --git a/lcs/agents/acs2/Effect.py b/lcs/agents/acs2/Effect.py index 7a2e0b79..5fcc49c1 100644 --- a/lcs/agents/acs2/Effect.py +++ b/lcs/agents/acs2/Effect.py @@ -45,7 +45,7 @@ def is_specializable(self, p0: Perception, p1: Perception) -> bool: return True - def get_best_anticipation(self, perception): + def get_best_anticipation(self, perception: Perception): """ Returns the most probable anticipation of the effect part. This is usually the normal anticipation. However, if PEEs are @@ -62,8 +62,9 @@ def get_best_anticipation(self, perception): ant[idx] = item return Perception(ant) - def does_specify_only_changes_backwards(self, back_anticipation, - situation): + def does_specify_only_changes_backwards(self, + back_anticipation: Perception, + situation: Perception): """ Returns if the effect part specifies at least one of the percepts. An PEE attribute never specifies the corresponding percept. @@ -81,7 +82,7 @@ def does_specify_only_changes_backwards(self, back_anticipation, return False return True - def does_match(self, perception, other_perception): + def does_match(self, perception: Perception, other_perception: Perception): """ Returns if the effect matches the perception. Hereby, the specified attributes are compared with perception. From 6de71e1868e436318cb7ead12df3a606f5e12b58 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Tue, 25 Sep 2018 11:47:26 +0200 Subject: [PATCH 54/83] added type hinting in strategies/action_planning --- lcs/agents/acs2/ACS2.py | 9 ++++-- lcs/agents/acs2/Classifier.py | 8 ++--- lcs/agents/acs2/Condition.py | 2 +- lcs/agents/acs2/Effect.py | 8 +++-- .../action_planning/action_planning.py | 6 ++-- .../action_planning/goal_sequence_searcher.py | 31 +++++++++++++------ 6 files changed, 42 insertions(+), 22 deletions(-) diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index 756bca1b..23380bb5 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -208,8 +208,13 @@ def _run_trial_exploit(self, env, time=None, current_trial=None): return steps - def _run_action_planning(self, env, time, situation, - previous_situation, action_set, action, reward): + def _run_action_planning(self, env, + time: int, + situation: str, + previous_situation: str, + action_set: ClassifiersList, + action: int, + reward: int): """ Executes action planning for model learning speed up. Method requests goals from 'goal generator' provided by diff --git a/lcs/agents/acs2/Classifier.py b/lcs/agents/acs2/Classifier.py index ca569257..11c89f77 100644 --- a/lcs/agents/acs2/Classifier.py +++ b/lcs/agents/acs2/Classifier.py @@ -397,7 +397,7 @@ def does_subsume(self, other: Classifier) -> bool: def is_marked(self): return self.mark.is_marked() - def does_match(self, situation: Perception): + def does_match(self, situation: Perception) -> bool: """ Returns if the classifier matches the situation. :param situation: @@ -405,7 +405,7 @@ def does_match(self, situation: Perception): """ return self.condition.does_match(situation) - def does_match_backwards(self, situation: Perception): + def does_match_backwards(self, situation: Perception) -> bool: """ Returns if 'situation' is matched by the anticipations. This is only the case if the specified conditions that have #-symbols @@ -418,7 +418,7 @@ def does_match_backwards(self, situation: Perception): return True return False - def get_best_anticipation(self, perception: Perception): + def get_best_anticipation(self, perception: Perception) -> Perception: """ Returns the anticipation, the classifier believes to happen most probably. This is usually the normal anticipation. @@ -429,7 +429,7 @@ def get_best_anticipation(self, perception: Perception): """ return self.effect.get_best_anticipation(perception) - def get_backwards_anticipation(self, perception: Perception): + def get_backwards_anticipation(self, perception: Perception) -> Perception: """ Returns the backwards anticipation. Returns -1 if the backwards anticipation was impossible to create. diff --git a/lcs/agents/acs2/Condition.py b/lcs/agents/acs2/Condition.py index 79311da6..9d5d338d 100644 --- a/lcs/agents/acs2/Condition.py +++ b/lcs/agents/acs2/Condition.py @@ -69,7 +69,7 @@ def does_match(self, other: Union[Perception, Condition]) -> bool: return True - def get_backwards_anticipation(self, perception: Perception): + def get_backwards_anticipation(self, perception: Perception) -> Perception: """ Returns the believed backwards anticipation. Hereby, the condition is treated like an effect part. diff --git a/lcs/agents/acs2/Effect.py b/lcs/agents/acs2/Effect.py index 5fcc49c1..252a9cc6 100644 --- a/lcs/agents/acs2/Effect.py +++ b/lcs/agents/acs2/Effect.py @@ -45,7 +45,7 @@ def is_specializable(self, p0: Perception, p1: Perception) -> bool: return True - def get_best_anticipation(self, perception: Perception): + def get_best_anticipation(self, perception: Perception) -> Perception: """ Returns the most probable anticipation of the effect part. This is usually the normal anticipation. However, if PEEs are @@ -64,7 +64,7 @@ def get_best_anticipation(self, perception: Perception): def does_specify_only_changes_backwards(self, back_anticipation: Perception, - situation: Perception): + situation: Perception) -> bool: """ Returns if the effect part specifies at least one of the percepts. An PEE attribute never specifies the corresponding percept. @@ -82,7 +82,9 @@ def does_specify_only_changes_backwards(self, return False return True - def does_match(self, perception: Perception, other_perception: Perception): + def does_match(self, + perception: Perception, + other_perception: Perception) -> bool: """ Returns if the effect matches the perception. Hereby, the specified attributes are compared with perception. diff --git a/lcs/strategies/action_planning/action_planning.py b/lcs/strategies/action_planning/action_planning.py index 3e13ed26..aeb07865 100644 --- a/lcs/strategies/action_planning/action_planning.py +++ b/lcs/strategies/action_planning/action_planning.py @@ -8,7 +8,7 @@ def exists_classifier(classifiers: ClassifiersList, previous_situation: Perception, action: int, situation: Perception, - quality: float): + quality: float) -> bool: """ Returns True if there is a classifier in this list with a quality higher than 'quality' that matches previous_situation, @@ -32,7 +32,7 @@ def exists_classifier(classifiers: ClassifiersList, def get_quality_classifiers_list(classifiers: ClassifiersList, quality: float, - cfg: Configuration = None): + cfg: Configuration = None) -> ClassifiersList: """ Constructs classifier list out of a list with q > quality. :param classifiers: @@ -49,7 +49,7 @@ def get_quality_classifiers_list(classifiers: ClassifiersList, def search_goal_sequence(classifiers: ClassifiersList, start: Perception, - goal: Perception): + goal: Perception) -> list: """ Searches a path from start to goal using a bidirectional method in the environmental model (i.e. the list of reliable classifiers). diff --git a/lcs/strategies/action_planning/goal_sequence_searcher.py b/lcs/strategies/action_planning/goal_sequence_searcher.py index 18b764a9..f1f400cb 100644 --- a/lcs/strategies/action_planning/goal_sequence_searcher.py +++ b/lcs/strategies/action_planning/goal_sequence_searcher.py @@ -1,5 +1,7 @@ from lcs import Perception +from lcs.agents.acs2 import Configuration from lcs.agents.acs2.ClassifiersList import ClassifiersList +from typing import List class GoalSequenceSearcher: @@ -9,7 +11,10 @@ def __init__(self): self.forward_perceptions = [] self.backward_perceptions = [] - def search_goal_sequence(self, reliable_classifiers, start, goal): + def search_goal_sequence(self, + reliable_classifiers: ClassifiersList, + start: str, + goal: str) -> list: """ Searches a path from start to goal using a bidirectional method in the environmental model (i.e. the list of reliable classifiers). @@ -60,8 +65,10 @@ def search_goal_sequence(self, reliable_classifiers, start, goal): # depth limit was reached -> return empty action sequence return [] - def _search_one_forward_step(self, reliable_classifiers, forward_size, - forward_point): + def _search_one_forward_step(self, + reliable_classifiers: ClassifiersList, + forward_size: int, + forward_point: int): """ Serches one step forward in the reliable_classifiers classifier list. Returns None if nothing was found so far, a sequence with a -1 element @@ -104,8 +111,9 @@ def _search_one_forward_step(self, reliable_classifiers, forward_size, i, backward_sequence_idx, match_set_element), size return None, size - def _search_one_backward_step(self, reliable_classifiers, backward_size, - backward_point): + def _search_one_backward_step(self, reliable_classifiers: ClassifiersList, + backward_size: int, + backward_point: int): """ Searches one step backward in the reliable_classifiers classifiers list Returns None if nothing was found so far, a sequence with a -1 element @@ -149,7 +157,9 @@ def _search_one_backward_step(self, reliable_classifiers, backward_size, return None, size @staticmethod - def _form_new_classifiers(classifiers_lists, i, match_set_el, cfg): + def _form_new_classifiers(classifiers_lists: List[ClassifiersList], i: int, + match_set_el: ClassifiersList, + cfg: Configuration) -> ClassifiersList: """ Executes actions after sequence was not detected. :param classifiers_lists: list of ClassifiersLists @@ -165,7 +175,8 @@ def _form_new_classifiers(classifiers_lists, i, match_set_el, cfg): new_classifiers.append(match_set_el) return new_classifiers - def _form_sequence_forwards(self, i, backward_sequence_idx, match_set_el): + def _form_sequence_forwards(self, i: int, backward_sequence_idx: int, + match_set_el: ClassifiersList) -> list: """ Forms sequence when it was found forwards. :param i: @@ -198,7 +209,8 @@ def _form_sequence_forwards(self, i, backward_sequence_idx, match_set_el): act_seq[k + j] = cl.action return act_seq - def _form_sequence_backwards(self, i, forward_sequence_idx, match_set_el): + def _form_sequence_backwards(self, i: int, forward_sequence_idx: int, + match_set_el: ClassifiersList) -> list: """ Forms sequence when it was found backwards. :param i: int @@ -232,7 +244,8 @@ def _form_sequence_backwards(self, i, forward_sequence_idx, match_set_el): return act_seq @staticmethod - def does_contain_state(perceptions, state): + def does_contain_state(perceptions: List[Perception], + state: Perception) -> int: """ Returns the position in the perception list where 'state' is stored or None if state is not found From bc8b85162c1547f3788d1239d156bb66dd49a457 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 26 Sep 2018 10:11:15 +0200 Subject: [PATCH 55/83] type hinting is now in every function --- lcs/agents/acs2/ACS2.py | 4 ++- lcs/agents/acs2/Classifier.py | 3 ++- lcs/agents/acs2/ClassifiersList.py | 2 +- .../action_planning/action_planning.py | 4 +-- .../action_planning/goal_sequence_searcher.py | 26 ++++++++++++------- 5 files changed, 24 insertions(+), 15 deletions(-) diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index 23380bb5..77920357 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -8,6 +8,7 @@ from ...agents.Agent import Metric from ...strategies.action_selection import choose_action from ...utils import parse_state, parse_action +from typing import Tuple class ACS2(Agent): @@ -214,7 +215,8 @@ def _run_action_planning(self, env, previous_situation: str, action_set: ClassifiersList, action: int, - reward: int): + reward: int) -> Tuple[int, str, str, + ClassifiersList, int]: """ Executes action planning for model learning speed up. Method requests goals from 'goal generator' provided by diff --git a/lcs/agents/acs2/Classifier.py b/lcs/agents/acs2/Classifier.py index 11c89f77..7afb92ed 100644 --- a/lcs/agents/acs2/Classifier.py +++ b/lcs/agents/acs2/Classifier.py @@ -429,7 +429,8 @@ def get_best_anticipation(self, perception: Perception) -> Perception: """ return self.effect.get_best_anticipation(perception) - def get_backwards_anticipation(self, perception: Perception) -> Perception: + def get_backwards_anticipation(self, perception: Perception) \ + -> Optional[Perception]: """ Returns the backwards anticipation. Returns -1 if the backwards anticipation was impossible to create. diff --git a/lcs/agents/acs2/ClassifiersList.py b/lcs/agents/acs2/ClassifiersList.py index a92b38d9..055ee451 100644 --- a/lcs/agents/acs2/ClassifiersList.py +++ b/lcs/agents/acs2/ClassifiersList.py @@ -30,7 +30,7 @@ def form_match_set(self, def form_match_set_backwards(self, situation: Perception, - cfg: Configuration): + cfg: Configuration) -> ClassifiersList: matching = [cl for cl in self if cl.does_match_backwards(situation)] return ClassifiersList(*matching, cfg=cfg) diff --git a/lcs/strategies/action_planning/action_planning.py b/lcs/strategies/action_planning/action_planning.py index aeb07865..57cf2d84 100644 --- a/lcs/strategies/action_planning/action_planning.py +++ b/lcs/strategies/action_planning/action_planning.py @@ -48,8 +48,8 @@ def get_quality_classifiers_list(classifiers: ClassifiersList, def search_goal_sequence(classifiers: ClassifiersList, - start: Perception, - goal: Perception) -> list: + start: str, + goal: str) -> list: """ Searches a path from start to goal using a bidirectional method in the environmental model (i.e. the list of reliable classifiers). diff --git a/lcs/strategies/action_planning/goal_sequence_searcher.py b/lcs/strategies/action_planning/goal_sequence_searcher.py index f1f400cb..69f77876 100644 --- a/lcs/strategies/action_planning/goal_sequence_searcher.py +++ b/lcs/strategies/action_planning/goal_sequence_searcher.py @@ -1,7 +1,7 @@ from lcs import Perception from lcs.agents.acs2 import Configuration from lcs.agents.acs2.ClassifiersList import ClassifiersList -from typing import List +from typing import List, Optional, Tuple class GoalSequenceSearcher: @@ -68,7 +68,8 @@ def search_goal_sequence(self, def _search_one_forward_step(self, reliable_classifiers: ClassifiersList, forward_size: int, - forward_point: int): + forward_point: int) -> Tuple[Optional[list], + int]: """ Serches one step forward in the reliable_classifiers classifier list. Returns None if nothing was found so far, a sequence with a -1 element @@ -111,9 +112,11 @@ def _search_one_forward_step(self, i, backward_sequence_idx, match_set_element), size return None, size - def _search_one_backward_step(self, reliable_classifiers: ClassifiersList, + def _search_one_backward_step(self, + reliable_classifiers: ClassifiersList, backward_size: int, - backward_point: int): + backward_point: int) -> Tuple[Optional[list], + int]: """ Searches one step backward in the reliable_classifiers classifiers list Returns None if nothing was found so far, a sequence with a -1 element @@ -157,7 +160,8 @@ def _search_one_backward_step(self, reliable_classifiers: ClassifiersList, return None, size @staticmethod - def _form_new_classifiers(classifiers_lists: List[ClassifiersList], i: int, + def _form_new_classifiers(classifiers_lists: List[ClassifiersList], + i: int, match_set_el: ClassifiersList, cfg: Configuration) -> ClassifiersList: """ @@ -175,8 +179,9 @@ def _form_new_classifiers(classifiers_lists: List[ClassifiersList], i: int, new_classifiers.append(match_set_el) return new_classifiers - def _form_sequence_forwards(self, i: int, backward_sequence_idx: int, - match_set_el: ClassifiersList) -> list: + def _form_sequence_forwards(self, i: int, + backward_sequence_idx: int, + match_set_el: ClassifiersList) -> List[int]: """ Forms sequence when it was found forwards. :param i: @@ -209,8 +214,9 @@ def _form_sequence_forwards(self, i: int, backward_sequence_idx: int, act_seq[k + j] = cl.action return act_seq - def _form_sequence_backwards(self, i: int, forward_sequence_idx: int, - match_set_el: ClassifiersList) -> list: + def _form_sequence_backwards(self, i: int, + forward_sequence_idx: int, + match_set_el: ClassifiersList) -> List[int]: """ Forms sequence when it was found backwards. :param i: int @@ -245,7 +251,7 @@ def _form_sequence_backwards(self, i: int, forward_sequence_idx: int, @staticmethod def does_contain_state(perceptions: List[Perception], - state: Perception) -> int: + state: Perception) -> Optional[int]: """ Returns the position in the perception list where 'state' is stored or None if state is not found From 9edee9d83d5a79f6fc3b094a34e860fbb008a09b Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 26 Sep 2018 12:29:43 +0200 Subject: [PATCH 56/83] more changes because of the last merge --- lcs/agents/acs2/ACS2.py | 3 ++- lcs/agents/acs2/ClassifiersList.py | 7 +++---- lcs/strategies/action_planning.py | 15 ++++++--------- 3 files changed, 11 insertions(+), 14 deletions(-) diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index ed6fef85..ad7d6c4c 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -277,7 +277,8 @@ def _run_action_planning(self, env, time, state, break act_sequence = self.population.search_goal_sequence(state, - goal_situation) + goal_situation, + self.cfg) # Execute the found sequence and learn during executing i = 0 diff --git a/lcs/agents/acs2/ClassifiersList.py b/lcs/agents/acs2/ClassifiersList.py index b84dae01..dc09a40b 100644 --- a/lcs/agents/acs2/ClassifiersList.py +++ b/lcs/agents/acs2/ClassifiersList.py @@ -222,7 +222,7 @@ def exists_classifier(self, previous_situation, action, situation, return True return False - def get_quality_classifiers_list(self, quality, cfg=None): + def get_quality_classifiers_list(self, quality): """ Constructs classifier list out of a list with q > quality. :param quality: @@ -235,7 +235,7 @@ def get_quality_classifiers_list(self, quality, cfg=None): listp.append(item) return listp - def search_goal_sequence(self, start, goal): + def search_goal_sequence(self, start, goal, cfg): """ Searches a path from start to goal using a bidirectional method in the environmental model (i.e. the list of reliable classifiers). @@ -244,8 +244,7 @@ def search_goal_sequence(self, start, goal): :return: Sequence of actions """ reliable_classifiers = self. \ - get_quality_classifiers_list(quality=self.cfg.theta_r, - cfg=self.cfg) + get_quality_classifiers_list(quality=cfg.theta_r) return GoalSequenceSearcher().search_goal_sequence( reliable_classifiers, start, goal) diff --git a/lcs/strategies/action_planning.py b/lcs/strategies/action_planning.py index 9c8002fe..df31ca43 100644 --- a/lcs/strategies/action_planning.py +++ b/lcs/strategies/action_planning.py @@ -76,8 +76,7 @@ def _search_one_forward_step(self, reliable_classifiers, forward_size, size = forward_size for i in range(forward_point, forward_size): match_forward = reliable_classifiers. \ - form_match_set(situation=self.forward_perceptions[i], - cfg=reliable_classifiers.cfg) + form_match_set(situation=self.forward_perceptions[i]) for match_set_element in match_forward: anticipation = match_set_element. \ get_best_anticipation(self.forward_perceptions[i]) @@ -92,8 +91,8 @@ def _search_one_forward_step(self, reliable_classifiers, forward_size, self.forward_perceptions.append(anticipation) self.forward_classifiers.append( self._form_new_classifiers( - self.forward_classifiers, i, match_set_element, - reliable_classifiers.cfg)) + self.forward_classifiers, i, + match_set_element)) size += 1 if size > 10001: # logging.debug("Arrays are full") @@ -120,8 +119,7 @@ def _search_one_backward_step(self, reliable_classifiers, backward_size, size = backward_size for i in range(backward_point, backward_size): match_backward = reliable_classifiers.form_match_set_backwards( - situation=self.backward_perceptions[i], - cfg=reliable_classifiers.cfg) + situation=self.backward_perceptions[i]) for match_set_el in match_backward: anticipation = match_set_el. \ get_backwards_anticipation(self.backward_perceptions[i]) @@ -137,8 +135,7 @@ def _search_one_backward_step(self, reliable_classifiers, backward_size, self.backward_perceptions.append(anticipation) self.backward_classifiers.append( self._form_new_classifiers( - self.backward_classifiers, i, match_set_el, - reliable_classifiers.cfg)) + self.backward_classifiers, i, match_set_el)) size += 1 if size > 10001: # logging.debug("Arrays are full") @@ -149,7 +146,7 @@ def _search_one_backward_step(self, reliable_classifiers, backward_size, return None, size @staticmethod - def _form_new_classifiers(classifiers_lists, i, match_set_el, cfg): + def _form_new_classifiers(classifiers_lists, i, match_set_el): """ Executes actions after sequence was not detected. :param classifiers_lists: list of ClassifiersLists From a9fd61bdfa031fa853ec19b8ee495c808df2ecec Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 26 Sep 2018 12:54:30 +0200 Subject: [PATCH 57/83] merge with ap-rev-09-22 --- assets/ACS2/acs2++.cc | 4 + examples/acs2/handeye/utils.py | 14 +- lcs/Perception.py | 3 - lcs/agents/acs2/ACS2.py | 30 ++-- lcs/agents/acs2/Classifier.py | 9 +- lcs/agents/acs2/ClassifiersList.py | 49 ------- lcs/agents/acs2/Condition.py | 6 +- lcs/agents/acs2/Effect.py | 15 +- lcs/strategies/action_planning/__init__.py | 0 .../action_planning/action_planning.py | 67 +++++++++ .../goal_sequence_searcher.py} | 63 +++++---- tests/lcs/agents/acs2/test_Classifier.py | 70 ++++------ tests/lcs/agents/acs2/test_ClassifierList.py | 74 ---------- tests/lcs/agents/acs2/test_Effect.py | 129 +++++++----------- .../strategies/action_planning/__init__.py | 0 .../action_planning/test_action_planning.py | 91 ++++++++++++ .../test_goal_sequence_searcher.py} | 21 +-- 17 files changed, 328 insertions(+), 317 deletions(-) create mode 100644 lcs/strategies/action_planning/__init__.py create mode 100644 lcs/strategies/action_planning/action_planning.py rename lcs/strategies/{action_planning.py => action_planning/goal_sequence_searcher.py} (83%) create mode 100644 tests/lcs/strategies/action_planning/__init__.py create mode 100644 tests/lcs/strategies/action_planning/test_action_planning.py rename tests/lcs/strategies/{test_action_planning.py => action_planning/test_goal_sequence_searcher.py} (96%) diff --git a/assets/ACS2/acs2++.cc b/assets/ACS2/acs2++.cc index 8077e030..9b2e5696 100755 --- a/assets/ACS2/acs2++.cc +++ b/assets/ACS2/acs2++.cc @@ -311,6 +311,10 @@ int startActionPlanning(ClassifierList *population, Environment *env, int time, } else { Action **actSequence = population->searchGoalSequence(situation, goalSituation); int i; + //cout<"; + //for(act=actSequence[0], i=0; act!=0; ++i, act=actSequence[i]) + // cout<"; + //cout< int: def __repr__(self): return ' '.join(map(str, self)) - def __setitem__(self, i, o): - self._items[i] = o - def __eq__(self, other): for si, oi in zip(self, other): if si != oi: diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index ad7d6c4c..f135a860 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -1,11 +1,14 @@ import logging from typing import Optional +from lcs.strategies.action_planning.action_planning import \ + search_goal_sequence, exists_classifier from . import ClassifiersList, Configuration from ...agents import Agent from ...agents.Agent import Metric from ...strategies.action_selection import choose_action from ...utils import parse_state, parse_action +from typing import Tuple logger = logging.getLogger(__name__) @@ -110,7 +113,7 @@ def _run_trial_explore(self, env, time, current_trial=None): while not done: if self.cfg.do_action_planning and \ - (steps + time) % self.cfg.action_planning_frequency == 0: + self._time_for_action_planning(steps + time): # Action Planning for increased model learning steps_ap, state, prev_state, action_set, reward = \ self._run_action_planning(env, steps + time, state, @@ -241,8 +244,15 @@ def _run_trial_exploit(self, env, time=None, current_trial=None): return steps - def _run_action_planning(self, env, time, state, - prev_state, action_set, action, reward): + + def _run_action_planning(self, env, + time: int, + state: str, + prev_state: str, + action_set: ClassifiersList, + action: int, + reward: int) -> Tuple[int, str, str, + ClassifiersList, int]: """ Executes action planning for model learning speed up. Method requests goals from 'goal generator' provided by @@ -276,9 +286,8 @@ def _run_action_planning(self, env, time, state, if goal_situation is None: break - act_sequence = self.population.search_goal_sequence(state, - goal_situation, - self.cfg) + act_sequence = search_goal_sequence(self.population, state, + goal_situation, self.cfg) # Execute the found sequence and learn during executing i = 0 @@ -325,10 +334,8 @@ def _run_action_planning(self, env, time, state, prev_state = state state = parse_state(raw_state) - if not action_set.exists_classifier(prev_state, - action, - state, - self.cfg.theta_r): + if not exists_classifier(action_set, prev_state, + action, state, self.cfg.theta_r): # no reliable classifier was able to anticipate # such a change break @@ -341,6 +348,9 @@ def _run_action_planning(self, env, time, state, return steps, state, prev_state, action_set, reward + def _time_for_action_planning(self, time): + return time % self.cfg.action_planning_frequency == 0 + def _collect_agent_metrics(self, trial, steps, total_steps) -> Metric: return { 'population': len(self.population), diff --git a/lcs/agents/acs2/Classifier.py b/lcs/agents/acs2/Classifier.py index 682c58a0..2af5875e 100644 --- a/lcs/agents/acs2/Classifier.py +++ b/lcs/agents/acs2/Classifier.py @@ -349,7 +349,7 @@ def generalize_unchanging_condition_attribute( def is_marked(self): return self.mark.is_marked() - def does_match(self, situation): + def does_match(self, situation: Perception) -> bool: """ Returns if the classifier matches the situation. :param situation: @@ -357,7 +357,7 @@ def does_match(self, situation): """ return self.condition.does_match(situation) - def does_match_backwards(self, situation): + def does_match_backwards(self, situation: Perception) -> bool: """ Returns if 'situation' is matched by the anticipations. This is only the case if the specified conditions that have #-symbols @@ -370,7 +370,7 @@ def does_match_backwards(self, situation): return True return False - def get_best_anticipation(self, perception): + def get_best_anticipation(self, perception: Perception) -> Perception: """ Returns the anticipation, the classifier believes to happen most probably. This is usually the normal anticipation. @@ -381,7 +381,8 @@ def get_best_anticipation(self, perception): """ return self.effect.get_best_anticipation(perception) - def get_backwards_anticipation(self, perception): + def get_backwards_anticipation(self, perception: Perception) \ + -> Optional[Perception]: """ Returns the backwards anticipation. Returns -1 if the backwards anticipation was impossible to create. diff --git a/lcs/agents/acs2/ClassifiersList.py b/lcs/agents/acs2/ClassifiersList.py index dc09a40b..95a701b3 100644 --- a/lcs/agents/acs2/ClassifiersList.py +++ b/lcs/agents/acs2/ClassifiersList.py @@ -9,7 +9,6 @@ import lcs.strategies.genetic_algorithms as ga import lcs.strategies.reinforcement_learning as rl from lcs import Perception, TypedList -from lcs.strategies.action_planning import GoalSequenceSearcher from lcs.agents.acs2 import Configuration from . import Classifier @@ -200,51 +199,3 @@ def apply_ga(time: int, ga.add_classifier(child, p, population, match_set, action_set, do_subsumption, theta_exp) - - def exists_classifier(self, previous_situation, action, situation, - quality): - """ - Returns True if there is a classifier in this list with a quality - higher than 'quality' that matches previous_situation, - specifies action, and predicts situation. - Returns False otherwise. - :param previous_situation: - :param action: - :param situation: - :param quality: - :return: - """ - for cl in self: - if cl.q > quality and cl.does_match(previous_situation) \ - and cl.action == action \ - and cl.does_anticipate_correctly(previous_situation, - situation): - return True - return False - - def get_quality_classifiers_list(self, quality): - """ - Constructs classifier list out of a list with q > quality. - :param quality: - :param cfg: - :return: ClassifiersList with only quality classifiers. - """ - listp = ClassifiersList() - for item in self: - if item.q > quality: - listp.append(item) - return listp - - def search_goal_sequence(self, start, goal, cfg): - """ - Searches a path from start to goal using a bidirectional method in the - environmental model (i.e. the list of reliable classifiers). - :param start: Perception - :param goal: Perception - :return: Sequence of actions - """ - reliable_classifiers = self. \ - get_quality_classifiers_list(quality=cfg.theta_r) - - return GoalSequenceSearcher().search_goal_sequence( - reliable_classifiers, start, goal) diff --git a/lcs/agents/acs2/Condition.py b/lcs/agents/acs2/Condition.py index 64be4725..448167f1 100644 --- a/lcs/agents/acs2/Condition.py +++ b/lcs/agents/acs2/Condition.py @@ -72,15 +72,15 @@ def does_match(self, other: Union[Perception, Condition]) -> bool: def does_match_condition(self, other: Condition) -> bool: return self.does_match(other) - def get_backwards_anticipation(self, perception): + def get_backwards_anticipation(self, perception: Perception) -> Perception: """ Returns the believed backwards anticipation. Hereby, the condition is treated like an effect part. :param perception: :return: """ - ant = Perception(perception) + ant = list(perception) for idx, item in enumerate(self): if item != self.wildcard: ant[idx] = item - return ant + return Perception(ant) diff --git a/lcs/agents/acs2/Effect.py b/lcs/agents/acs2/Effect.py index 1fc7738e..252a9cc6 100644 --- a/lcs/agents/acs2/Effect.py +++ b/lcs/agents/acs2/Effect.py @@ -45,7 +45,7 @@ def is_specializable(self, p0: Perception, p1: Perception) -> bool: return True - def get_best_anticipation(self, perception): + def get_best_anticipation(self, perception: Perception) -> Perception: """ Returns the most probable anticipation of the effect part. This is usually the normal anticipation. However, if PEEs are @@ -56,14 +56,15 @@ def get_best_anticipation(self, perception): """ # TODO: implement the rest after PEEs are implemented # ('getBestChar' function) - ant = Perception(perception) + ant = list(perception) for idx, item in enumerate(self): if item != self.wildcard: ant[idx] = item - return ant + return Perception(ant) - def does_specify_only_changes_backwards(self, back_anticipation, - situation): + def does_specify_only_changes_backwards(self, + back_anticipation: Perception, + situation: Perception) -> bool: """ Returns if the effect part specifies at least one of the percepts. An PEE attribute never specifies the corresponding percept. @@ -81,7 +82,9 @@ def does_specify_only_changes_backwards(self, back_anticipation, return False return True - def does_match(self, perception, other_perception): + def does_match(self, + perception: Perception, + other_perception: Perception) -> bool: """ Returns if the effect matches the perception. Hereby, the specified attributes are compared with perception. diff --git a/lcs/strategies/action_planning/__init__.py b/lcs/strategies/action_planning/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/lcs/strategies/action_planning/action_planning.py b/lcs/strategies/action_planning/action_planning.py new file mode 100644 index 00000000..a87a0bfb --- /dev/null +++ b/lcs/strategies/action_planning/action_planning.py @@ -0,0 +1,67 @@ +from lcs import Perception +from lcs.agents.acs2 import ClassifiersList, Configuration +from lcs.strategies.action_planning.goal_sequence_searcher \ + import GoalSequenceSearcher + + +def exists_classifier(classifiers: ClassifiersList, + previous_situation: Perception, + action: int, + situation: Perception, + quality: float) -> bool: + """ + Returns True if there is a classifier in this list with a quality + higher than 'quality' that matches previous_situation, + specifies action, and predicts situation. + Returns False otherwise. + :param classifiers: + :param previous_situation: + :param action: + :param situation: + :param quality: + :return: + """ + for cl in classifiers: + if cl.q > quality and cl.does_match(previous_situation) \ + and cl.action == action \ + and cl.does_anticipate_correctly(previous_situation, + situation): + return True + return False + + +def get_quality_classifiers_list(classifiers: ClassifiersList, + quality: float, + cfg: Configuration = None) -> ClassifiersList: + """ + Constructs classifier list out of a list with q > quality. + :param classifiers: + :param quality: + :param cfg: + :return: ClassifiersList with only quality classifiers. + """ + listp = ClassifiersList() + for item in classifiers: + if item.q > quality: + listp.append(item) + return listp + + +def search_goal_sequence(classifiers: ClassifiersList, + start: str, + goal: str, + cfg: Configuration) -> list: + """ + Searches a path from start to goal using a bidirectional method in the + environmental model (i.e. the list of reliable classifiers). + :param classifiers: + :param start: Perception + :param goal: Perception + :return: Sequence of actions + """ + reliable_classifiers = \ + get_quality_classifiers_list(classifiers, + quality=cfg.theta_r) + + return GoalSequenceSearcher().search_goal_sequence( + reliable_classifiers, start, goal) diff --git a/lcs/strategies/action_planning.py b/lcs/strategies/action_planning/goal_sequence_searcher.py similarity index 83% rename from lcs/strategies/action_planning.py rename to lcs/strategies/action_planning/goal_sequence_searcher.py index df31ca43..a5b20dae 100644 --- a/lcs/strategies/action_planning.py +++ b/lcs/strategies/action_planning/goal_sequence_searcher.py @@ -1,5 +1,7 @@ from lcs import Perception -from lcs.agents.acs2 import ClassifiersList +from lcs.agents.acs2 import Configuration, Classifier +from lcs.agents.acs2.ClassifiersList import ClassifiersList +from typing import List, Optional, Tuple class GoalSequenceSearcher: @@ -9,7 +11,10 @@ def __init__(self): self.forward_perceptions = [] self.backward_perceptions = [] - def search_goal_sequence(self, reliable_classifiers, start, goal): + def search_goal_sequence(self, + reliable_classifiers: ClassifiersList, + start: str, + goal: str) -> list: """ Searches a path from start to goal using a bidirectional method in the environmental model (i.e. the list of reliable classifiers). @@ -19,7 +24,7 @@ def search_goal_sequence(self, reliable_classifiers, start, goal): :return: Sequence of actions """ if len(reliable_classifiers) < 1: - return self.empty_sequence() + return [] max_depth = 6 forward_size = 1 @@ -58,10 +63,13 @@ def search_goal_sequence(self, reliable_classifiers, start, goal): return action_sequence # depth limit was reached -> return empty action sequence - return self.empty_sequence() + return [] - def _search_one_forward_step(self, reliable_classifiers, forward_size, - forward_point): + def _search_one_forward_step(self, + reliable_classifiers: ClassifiersList, + forward_size: int, + forward_point: int) -> Tuple[Optional[list], + int]: """ Serches one step forward in the reliable_classifiers classifier list. Returns None if nothing was found so far, a sequence with a -1 element @@ -96,15 +104,18 @@ def _search_one_forward_step(self, reliable_classifiers, forward_size, size += 1 if size > 10001: # logging.debug("Arrays are full") - return self.empty_sequence(), size + return [], size else: # sequence found return self._form_sequence_forwards( i, backward_sequence_idx, match_set_element), size return None, size - def _search_one_backward_step(self, reliable_classifiers, backward_size, - backward_point): + def _search_one_backward_step(self, + reliable_classifiers: ClassifiersList, + backward_size: int, + backward_point: int) -> Tuple[Optional[list], + int]: """ Searches one step backward in the reliable_classifiers classifiers list Returns None if nothing was found so far, a sequence with a -1 element @@ -139,14 +150,17 @@ def _search_one_backward_step(self, reliable_classifiers, backward_size, size += 1 if size > 10001: # logging.debug("Arrays are full") - return self.empty_sequence(), size + return [], size else: return self._form_sequence_backwards( i, forward_sequence_idx, match_set_el), size return None, size @staticmethod - def _form_new_classifiers(classifiers_lists, i, match_set_el): + def _form_new_classifiers(classifiers_lists: List[ClassifiersList], + i: int, + match_set_el: Classifier) \ + -> ClassifiersList: """ Executes actions after sequence was not detected. :param classifiers_lists: list of ClassifiersLists @@ -155,14 +169,16 @@ def _form_new_classifiers(classifiers_lists, i, match_set_el): :return: new size of classifiers """ if i > 0: - new_classifiers = ClassifiersList.ClassifiersList() + new_classifiers = ClassifiersList() new_classifiers.extend(classifiers_lists[i - 1]) else: - new_classifiers = ClassifiersList.ClassifiersList() + new_classifiers = ClassifiersList() new_classifiers.append(match_set_el) return new_classifiers - def _form_sequence_forwards(self, i, backward_sequence_idx, match_set_el): + def _form_sequence_forwards(self, i: int, + backward_sequence_idx: int, + match_set_el: Classifier) -> List[int]: """ Forms sequence when it was found forwards. :param i: @@ -195,7 +211,9 @@ def _form_sequence_forwards(self, i, backward_sequence_idx, match_set_el): act_seq[k + j] = cl.action return act_seq - def _form_sequence_backwards(self, i, forward_sequence_idx, match_set_el): + def _form_sequence_backwards(self, i: int, + forward_sequence_idx: int, + match_set_el: Classifier) -> List[int]: """ Forms sequence when it was found backwards. :param i: int @@ -229,7 +247,8 @@ def _form_sequence_backwards(self, i, forward_sequence_idx, match_set_el): return act_seq @staticmethod - def does_contain_state(perceptions, state): + def does_contain_state(perceptions: List[Perception], + state: Perception) -> Optional[int]: """ Returns the position in the perception list where 'state' is stored or None if state is not found @@ -241,15 +260,3 @@ def does_contain_state(perceptions, state): if percept == state: return i return None - - @staticmethod - def empty_sequence(): - """ - Returns empty sequence. - This function might be deleted in later versions of code, but it is - useful if we decide to change the definition - of an empty sequence (it used to be [-1], because it was more alike - the original code in C++). - :return: empty action sequence - """ - return [] diff --git a/tests/lcs/agents/acs2/test_Classifier.py b/tests/lcs/agents/acs2/test_Classifier.py index a2eab63b..b2d83d93 100644 --- a/tests/lcs/agents/acs2/test_Classifier.py +++ b/tests/lcs/agents/acs2/test_Classifier.py @@ -710,74 +710,54 @@ def test_should_generalize_second_unchanging_attribute(self, cfg): assert len(cls.specified_unchanging_attributes) == 1 assert Condition('#####0##') == cls.condition - def test_does_match_backwards(self, cfg): + @pytest.mark.parametrize("_e, _result", [ + ('#####1#1', True), + ('0######1', False), + ('#####0#1', False) + ]) + def test_does_match_backwards(self, cfg, _e, _result): # given percept = Perception('11111111') - - # C1 - matching - c1 = Classifier( - condition='#####0#0', - effect='#####1#1', - cfg=cfg) - - # C2 - non-matching - c2 = Classifier( - condition='#####0#0', - effect='0######1', - cfg=cfg) - - # C3 - non-matching - c3 = Classifier( - condition='#####0#0', - effect='#####0#1', - cfg=cfg) + c1 = Classifier(condition='#####0#0', effect=_e, cfg=cfg) # when result1 = c1.does_match_backwards(percept) - result2 = c2.does_match_backwards(percept) - result3 = c3.does_match_backwards(percept) # then - assert result1 is True - assert result2 is False - assert result3 is False + assert result1 is _result - def test_get_backwards_anticipation(self, cfg): + @pytest.mark.parametrize("_c, _e, _result", [ + ('#0###1##', '#1###0##', ['0', '0', '1', '1', '1', '1', '1', '0']), + ('#0###1##', '#0###0##', None), + ('#0###1##', '0####0##', None) + ]) + def test_get_backwards_anticipation(self, cfg, _c, _e, _result): # given p0 = Perception(['0', '1', '1', '1', '1', '0', '1', '0']) - - # C0 - OK - c0 = Classifier(condition='#0###1##', effect='#1###0##', cfg=cfg) - - # C1 - not OK - c1 = Classifier(condition='#0###1##', effect='#0###0##', cfg=cfg) - - # C1 - not OK - c2 = Classifier(condition='#0###1##', effect='0####0##', cfg=cfg) + c0 = Classifier(condition=_c, effect=_e, cfg=cfg) # when result0 = c0.get_backwards_anticipation(p0) - result1 = c1.get_backwards_anticipation(p0) - result2 = c2.get_backwards_anticipation(p0) # then - assert result0 == ['0', '0', '1', '1', '1', '1', '1', '0'] - assert result1 is None - assert result2 is None + assert result0 == _result - def test_get_best_anticipation(self, cfg): + @pytest.mark.parametrize("_p0, _result", [ + (['1', '1', '0', '1', '1', '1', '1', '1'], + ['1', '1', '0', '1', '1', '0', '1', '1']), + (['1', '1', '1', '1', '1', '1', '1', '1'], + ['1', '1', '0', '1', '1', '0', '1', '1']) + ]) + def test_get_best_anticipation(self, cfg, _p0, _result): # given - p0 = Perception(['1', '1', '0', '1', '1', '1', '1', '1']) - p1 = Perception(['1', '1', '1', '1', '1', '1', '1', '1']) + p0 = Perception(_p0) classifier = Classifier(effect='##0##0##', cfg=cfg) # when result0 = classifier.get_best_anticipation(p0) - result1 = classifier.get_best_anticipation(p1) # then - assert result0 == ['1', '1', '0', '1', '1', '0', '1', '1'] - assert result1 == ['1', '1', '0', '1', '1', '0', '1', '1'] + assert result0 == _result @pytest.mark.parametrize("_c, _p, _result", [ ('########', '10011001', True), diff --git a/tests/lcs/agents/acs2/test_ClassifierList.py b/tests/lcs/agents/acs2/test_ClassifierList.py index 49a2a161..27f5f62b 100644 --- a/tests/lcs/agents/acs2/test_ClassifierList.py +++ b/tests/lcs/agents/acs2/test_ClassifierList.py @@ -149,77 +149,3 @@ def test_should_form_match_set_backwards(self, cfg): assert c1 in match_set assert c2 in match_set - def test_get_quality_classifiers_list(self, cfg): - # given - population = ClassifiersList() - - # C1 - matching - c1 = Classifier(quality=0.9, cfg=cfg) - - # C2 - matching - c2 = Classifier(quality=0.7, cfg=cfg) - - # C3 - non-matching - c3 = Classifier(quality=0.5, cfg=cfg) - - # C4 - non-matching - c4 = Classifier(quality=0.1, cfg=cfg) - - population.append(c1) - population.append(c2) - population.append(c3) - population.append(c4) - - # when - match_set = population.get_quality_classifiers_list(0.5, cfg) - # then - assert 2 == len(match_set) - assert c1 in match_set - assert c2 in match_set - - def test_exists_classifier(self, cfg): - # given - population = ClassifiersList() - prev_situation = Perception('01100000') - situation = Perception('11110000') - act = 0 - q = 0.5 - - # C1 - OK - c1 = Classifier(condition='0##0####', action=0, effect='1##1####', - quality=0.7, cfg=cfg) - - # C2 - wrong action - c2 = Classifier(condition='0##0####', action=1, effect='1##1####', - quality=0.7, cfg=cfg) - - # C3 - wrong condition - c3 = Classifier(condition='0##1####', action=0, effect='1##1####', - quality=0.7, cfg=cfg) - - # C4 - wrong effect - c4 = Classifier(condition='0##0####', action=0, effect='1##0####', - quality=0.7, cfg=cfg) - - # C5 - wrong quality - c5 = Classifier(condition='0##0####', action=0, effect='1##1####', - quality=0.25, cfg=cfg) - - population.append(c2) - population.append(c3) - population.append(c4) - population.append(c5) - - # when - result0 = population.\ - exists_classifier(previous_situation=prev_situation, - situation=situation, action=act, quality=q) - - population.append(c1) - result1 = population.\ - exists_classifier(previous_situation=prev_situation, - situation=situation, action=act, quality=q) - - # then - assert result0 is False - assert result1 is True diff --git a/tests/lcs/agents/acs2/test_Effect.py b/tests/lcs/agents/acs2/test_Effect.py index 671e492f..d09057f8 100644 --- a/tests/lcs/agents/acs2/test_Effect.py +++ b/tests/lcs/agents/acs2/test_Effect.py @@ -116,100 +116,75 @@ def test_eq(self): assert Effect('00001111') == Effect('00001111') assert Effect('00001111') != Effect('0000111#') - def test_does_match(self): - # given - p0 = Perception(['1', '1', '0', '1', '1', '1', '0', '1']) - p1 = Perception(['1', '1', '1', '1', '1', '0', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) - - # when - result = effect.does_match(p0, p1) - - # then - assert result is True - - def test_does_match_false(self): - # given - p0 = Perception(['1', '0', '1', '1', '1', '0', '0', '1']) - p1 = Perception(['1', '1', '0', '1', '1', '1', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) - - # when - result = effect.does_match(p0, p1) - - # then - assert result is False - - def test_does_match_false_2(self): - # given - p0 = Perception(['1', '1', '1', '1', '0', '0', '0', '1']) - p1 = Perception(['1', '1', '0', '1', '1', '1', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) - - # when - result = effect.does_match(p0, p1) - - # then - assert result is False - - def test_does_match_false_3(self): + @pytest.mark.parametrize("_p0, _p1, _e, _result", [ + (['1', '1', '0', '1', '1', '1', '0', '1'], + ['1', '1', '1', '1', '1', '0', '0', '1'], + ['#', '#', '0', '#', '#', '1', '#', '#'], + True), + (['1', '0', '1', '1', '1', '0', '0', '1'], + ['1', '1', '0', '1', '1', '1', '0', '1'], + ['#', '#', '0', '#', '#', '1', '#', '#'], + False), + (['1', '1', '1', '1', '0', '0', '0', '1'], + ['1', '1', '0', '1', '1', '1', '0', '1'], + ['#', '#', '0', '#', '#', '1', '#', '#'], + False), + (['1', '1', '1', '1', '1', '0', '1', '1'], + ['1', '1', '1', '1', '1', '1', '0', '1'], + ['#', '#', '0', '#', '#', '1', '#', '#'], + False) + ]) + def test_does_match(self, _p0, _p1, _e, _result): # given - p0 = Perception(['1', '1', '1', '1', '1', '0', '1', '1']) - p1 = Perception(['1', '1', '1', '1', '1', '1', '0', '1']) - effect = Effect(['#', '#', '0', '#', '#', '1', '#', '#']) + p0 = Perception(_p0) + p1 = Perception(_p1) + effect = Effect(_e) # when result = effect.does_match(p0, p1) # then - assert result is False + assert result is _result - def test_get_best_anticipation(self): + @pytest.mark.parametrize("_p0, _result", [ + (['1', '1', '0', '1', '1', '1', '1', '1'], + ['1', '1', '0', '1', '1', '0', '1', '1']), + (['1', '1', '1', '1', '1', '1', '1', '1'], + ['1', '1', '0', '1', '1', '0', '1', '1']) + ]) + def test_get_best_anticipation(self, _p0, _result): # given - p0 = Perception(['1', '1', '0', '1', '1', '1', '1', '1']) - p1 = Perception(['1', '1', '1', '1', '1', '1', '1', '1']) + p0 = Perception(_p0) effect = Effect(['#', '#', '0', '#', '#', '0', '#', '#']) # when result0 = effect.get_best_anticipation(p0) - result1 = effect.get_best_anticipation(p1) - - # then - assert result0 == ['1', '1', '0', '1', '1', '0', '1', '1'] - assert result1 == ['1', '1', '0', '1', '1', '0', '1', '1'] - - def test_does_specify_only_changes_backwards(self): - # given - back_ant = Perception(['1', '1', '0', '1', '1', '1', '1', '0']) - sit = Perception(['1', '1', '1', '1', '1', '1', '1', '0']) - effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#']) - - # when - result = effect.does_specify_only_changes_backwards(back_ant, sit) # then - assert result is True - - def test_does_specify_only_changes_backwards_false(self): - # given - back_ant = Perception(['1', '1', '0', '1', '1', '1', '1', '0']) - sit = Perception(['1', '1', '1', '1', '1', '1', '0', '0']) - effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#']) - - # when - result = effect.does_specify_only_changes_backwards(back_ant, sit) - - # then - assert result is False - - def test_does_specify_only_changes_backwards_false_2(self): + assert result0 == _result + + @pytest.mark.parametrize("_p0, _p1, _e, _result", [ + (['1', '1', '0', '1', '1', '1', '1', '0'], + ['1', '1', '1', '1', '1', '1', '1', '0'], + ['#', '#', '1', '#', '#', '0', '#', '#'], + True), + (['1', '1', '0', '1', '1', '1', '1', '0'], + ['1', '1', '1', '1', '1', '1', '0', '0'], + ['#', '#', '1', '#', '#', '0', '#', '#'], + False), + (['1', '1', '0', '1', '1', '0', '1', '0'], + ['1', '1', '1', '1', '1', '1', '1', '0'], + ['#', '#', '1', '#', '#', '0', '#', '#'], + False) + ]) + def test_does_specify_only_changes_backwards(self, _p0, _p1, _e, _result): # given - back_ant = Perception(['1', '1', '0', '1', '1', '0', '1', '0']) - sit = Perception(['1', '1', '1', '1', '1', '1', '1', '0']) - effect = Effect(['#', '#', '1', '#', '#', '0', '#', '#']) + back_ant = Perception(_p0) + sit = Perception(_p1) + effect = Effect(_e) # when result = effect.does_specify_only_changes_backwards(back_ant, sit) # then - assert result is False + assert result is _result diff --git a/tests/lcs/strategies/action_planning/__init__.py b/tests/lcs/strategies/action_planning/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/lcs/strategies/action_planning/test_action_planning.py b/tests/lcs/strategies/action_planning/test_action_planning.py new file mode 100644 index 00000000..e58377f4 --- /dev/null +++ b/tests/lcs/strategies/action_planning/test_action_planning.py @@ -0,0 +1,91 @@ +from random import randint + +import pytest + +from lcs import Perception +from lcs.agents.acs2 import Configuration, ClassifiersList, Classifier +from lcs.strategies.action_planning.action_planning import \ + get_quality_classifiers_list, exists_classifier + + +class TestActionPlanning: + + @pytest.fixture + def cfg(self): + return Configuration(8, 8) + + def test_get_quality_classifiers_list(self, cfg): + # given + population = ClassifiersList() + + # C1 - matching + c1 = Classifier(quality=0.9, cfg=cfg) + + # C2 - matching + c2 = Classifier(quality=0.7, cfg=cfg) + + # C3 - non-matching + c3 = Classifier(quality=0.5, cfg=cfg) + + # C4 - non-matching + c4 = Classifier(quality=0.1, cfg=cfg) + + population.append(c1) + population.append(c2) + population.append(c3) + population.append(c4) + + # when + match_set = get_quality_classifiers_list(population, 0.5, cfg) + + # then + assert 2 == len(match_set) + assert c1 in match_set + assert c2 in match_set + + def test_exists_classifier(self, cfg): + # given + population = ClassifiersList() + prev_situation = Perception('01100000') + situation = Perception('11110000') + act = 0 + q = 0.5 + + # C1 - OK + c1 = Classifier(condition='0##0####', action=0, effect='1##1####', + quality=0.7, cfg=cfg) + + # C2 - wrong action + c2 = Classifier(condition='0##0####', action=1, effect='1##1####', + quality=0.7, cfg=cfg) + + # C3 - wrong condition + c3 = Classifier(condition='0##1####', action=0, effect='1##1####', + quality=0.7, cfg=cfg) + + # C4 - wrong effect + c4 = Classifier(condition='0##0####', action=0, effect='1##0####', + quality=0.7, cfg=cfg) + + # C5 - wrong quality + c5 = Classifier(condition='0##0####', action=0, effect='1##1####', + quality=0.25, cfg=cfg) + + population.append(c2) + population.append(c3) + population.append(c4) + population.append(c5) + + # when + result0 = exists_classifier(population, + previous_situation=prev_situation, + situation=situation, action=act, quality=q) + + population.append(c1) + result1 = exists_classifier(population, + previous_situation=prev_situation, + situation=situation, action=act, quality=q) + + # then + assert result0 is False + assert result1 is True diff --git a/tests/lcs/strategies/test_action_planning.py b/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py similarity index 96% rename from tests/lcs/strategies/test_action_planning.py rename to tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py index 65cd166b..baf8ed1d 100644 --- a/tests/lcs/strategies/test_action_planning.py +++ b/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py @@ -1,14 +1,12 @@ -from random import randint - import pytest from lcs import Perception from lcs.agents.acs2 import Configuration, ClassifiersList, Classifier -from lcs.strategies.action_planning import GoalSequenceSearcher - +from lcs.strategies.action_planning.goal_sequence_searcher \ + import GoalSequenceSearcher -class TestActionPlanning: +class TestGoalSequenceSearcher: @pytest.fixture def cfg(self): return Configuration(8, 8) @@ -32,13 +30,6 @@ def test_does_contain_state(self): assert result_1 == 1 assert result_none is None - def test_empty_sequence(self): - # given, when - seq = GoalSequenceSearcher.empty_sequence() - - # then - assert seq == [] - def test_search_goal_sequence_1(self): # given gs = GoalSequenceSearcher() @@ -50,7 +41,7 @@ def test_search_goal_sequence_1(self): # when result = gs.search_goal_sequence(empty_list, start=start, goal=goal) - assert result == gs.empty_sequence() + assert result == [] @pytest.mark.skip(reason="not implemented yet") def test_search_goal_sequence(self): @@ -65,7 +56,7 @@ def test_form_new_classifiers_1(self, cfg): i = 0 # when - new_classifiers = gs._form_new_classifiers(cls_list, i, cl, cfg) + new_classifiers = gs._form_new_classifiers(cls_list, i, cl) # then assert len(new_classifiers) == 1 @@ -82,7 +73,7 @@ def test_form_new_classifiers_2(self, cfg): i = 1 # when - new_classifiers = gs._form_new_classifiers(cls_list, i, cl1, cfg) + new_classifiers = gs._form_new_classifiers(cls_list, i, cl1) # then assert len(new_classifiers) == 2 From 8f86eb6f5d711265e1f3670f01b147eda490777c Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 26 Sep 2018 16:14:50 +0200 Subject: [PATCH 58/83] added notebooks --- .../notebooks/handeye/ACS2_in_HandEye.ipynb | 371 ++++++++++++++++++ ...dEye2-v0_ap_2018-09-21 11:25:11.446657.pdf | Bin ...e2-v0_no_ap_2018-09-21 11:25:11.446657.pdf | Bin ...HandEye2_ap_2018-09-20 16:01:56.258596.pdf | Bin ...dEye2_no_ap_2018-09-20 16:01:56.258596.pdf | Bin ...dEye3-v0_ap_2018-09-21 11:27:24.223319.pdf | Bin ...dEye3-v0_ap_2018-09-21 11:41:38.637807.pdf | Bin ...e3-v0_no_ap_2018-09-21 11:27:24.223319.pdf | Bin ...e3-v0_no_ap_2018-09-21 11:41:38.637807.pdf | Bin ...HandEye3_ap_2018-09-20 16:04:37.462759.pdf | Bin ...dEye3_no_ap_2018-09-20 16:04:37.462759.pdf | Bin ...dEye4-v0_ap_2018-09-21 09:04:07.609873.pdf | Bin ...e4-v0_no_ap_2018-09-21 09:04:07.609873.pdf | Bin ...dEye5-v0_ap_2018-09-21 09:18:25.359776.pdf | Bin ...dEye5-v0_ap_2018-09-21 09:50:43.258562.pdf | Bin ...e5-v0_no_ap_2018-09-21 09:50:43.258562.pdf | Bin ...dEye6-v0_ap_2018-09-21 10:19:16.162872.pdf | Bin ...e6-v0_no_ap_2018-09-21 10:19:16.162872.pdf | Bin .../notebooks/handeye/images}/handeye.pdf | Bin .../notebooks/handeye/images}/handeye_ap.pdf | Bin .../handeye_ap_2018-09-20 15:51:58.908574.pdf | Bin .../handeye/images}/handeye_no_ap.pdf | Bin ...ndeye_no_ap_2018-09-20 15:51:58.908574.pdf | Bin .../source/notebooks}/handeye/plot_handeye.py | 2 +- 24 files changed, 372 insertions(+), 1 deletion(-) create mode 100644 docs/source/notebooks/handeye/ACS2_in_HandEye.ipynb rename {examples/acs2 => docs/source/notebooks}/handeye/images/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf (100%) rename {examples/acs2 => docs/source/notebooks}/handeye/images/HandEye2-v0_no_ap_2018-09-21 11:25:11.446657.pdf (100%) rename {examples/acs2 => 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15:51:58.908574.pdf (100%) rename {examples/acs2/handeye => docs/source/notebooks/handeye/images}/handeye_no_ap.pdf (100%) rename {examples/acs2 => docs/source/notebooks}/handeye/images/handeye_no_ap_2018-09-20 15:51:58.908574.pdf (100%) rename {examples/acs2 => docs/source/notebooks}/handeye/plot_handeye.py (98%) diff --git a/docs/source/notebooks/handeye/ACS2_in_HandEye.ipynb b/docs/source/notebooks/handeye/ACS2_in_HandEye.ipynb new file mode 100644 index 00000000..5c8d06c7 --- /dev/null +++ b/docs/source/notebooks/handeye/ACS2_in_HandEye.ipynb @@ -0,0 +1,371 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ACS2 in HandEye environment with Action Planning\n", + "This notebook presents how to use HandEye environment with and without Action Planning.\n", + "\n", + "All of ACS2 code can be found in a Github repository: https://github.com/ParrotPrediction/pyalcs. \n", + "\n", + "If you want to read more about HandEye envronment, it is described in an article by Martin Butz and Wolfgang Stolzmann: 'Action-Planning in Anticipatory Classifier Systems'. It also describes how Action Planning works.\n", + "\n", + "First, we need to import important dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import gym\n", + "import gym_handeye\n", + "from lcs.agents.acs2 import ACS2, Configuration\n", + "from examples.acs2.handeye.utils import calculate_performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment\n", + "\n", + "Then, we need to create an OpenAI Gym environment. It is located in a Github repository: https://github.com/ParrotPrediction/openai-envs. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "hand_eye = gym.make('HandEye3-v0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The number after the name of the environment (in this example - '3') stands for the size of the environment. You can use environments of size from 2 up to 9.\n", + "\n", + "After creating the environment, we need to configure the agent." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n,\n", + " epsilon=1.0,\n", + " do_ga=False,\n", + " do_action_planning=True,\n", + " performance_fcn=calculate_performance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, we set the 'do_action_planning' variable to True. You can of course use this environment without Action Plannin by simply setting this variable to False.\n", + "\n", + "Now we can just create the ACS2 agent to explore the environment:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + " # Explore the environment\n", + "agent = ACS2(cfg)\n", + "population, explore_metrics = agent.explore(hand_eye, 50)\n", + "\n", + "# Exploit the environment\n", + "agent = ACS2(cfg, population)\n", + "population, exploit_metric = agent.exploit(hand_eye, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating the environment and running an agent in it is really simple and can look really spectacular, but that's not the most interesting part.\n", + "\n", + "## Graphs\n", + "\n", + "If we want to compare the result from the HandEye environment with and without Action Planning, the most convenient way is to use graphs. Of course the ACS2 agent is not deterministic, but we won't worry about it now.\n", + "\n", + "To see how Action Planning helps the agent gain knowledge about the environment, we can compare three metrics: overall knowledge, knowledge about the transitions with block (grip the block, release the block or move the block), and knowledge about the transitions without block (moving the gripper without the block).\n", + "\n", + "First, let's import dependencies and set the constants:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "\n", + "import gym\n", + "import gym_handeye\n", + "\n", + "from lcs.agents.acs2 import ACS2, ClassifiersList, Configuration\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from examples.acs2.handeye.utils import calculate_performance\n", + "\n", + "TITLE_TEXT_SIZE=24\n", + "AXIS_TEXT_SIZE=18\n", + "LEGEND_TEXT_SIZE=16" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we need some functions to help in plotting the results we get from the environment." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def parse_metrics_to_df(explore_metrics, exploit_metrics):\n", + " def extract_details(row):\n", + " row['trial'] = row['agent']['trial']\n", + " row['steps'] = row['agent']['steps']\n", + " row['numerosity'] = row['agent']['numerosity']\n", + " row['reliable'] = row['agent']['reliable']\n", + " row['knowledge'] = row['performance']['knowledge']\n", + " row['with_block'] = row['performance']['with_block']\n", + " row['no_block'] = row['performance']['no_block']\n", + " return row\n", + "\n", + " # Load both metrics into data frame\n", + " explore_df = pd.DataFrame(explore_metrics)\n", + " exploit_df = pd.DataFrame(exploit_metrics)\n", + "\n", + " # Mark them with specific phase\n", + " explore_df['phase'] = 'explore'\n", + " exploit_df['phase'] = 'exploit'\n", + "\n", + " # Extract details\n", + " explore_df = explore_df.apply(extract_details, axis=1)\n", + " exploit_df = exploit_df.apply(extract_details, axis=1)\n", + "\n", + " # Adjuts exploit trial counter\n", + " exploit_df['trial'] = exploit_df.apply(\n", + " lambda r: r['trial'] + len(explore_df), axis=1)\n", + "\n", + " # Concatenate both dataframes\n", + " df = pd.concat([explore_df, exploit_df])\n", + " df.drop(['agent', 'environment', 'performance'], axis=1, inplace=True)\n", + " df.set_index('trial', inplace=True)\n", + "\n", + " return df\n", + "\n", + "\n", + "def plot_knowledge(df, ax=None):\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " explore_df = df.query(\"phase == 'explore'\")\n", + " exploit_df = df.query(\"phase == 'exploit'\")\n", + "\n", + " explore_df['knowledge'].plot(ax=ax, c='blue')\n", + " explore_df['with_block'].plot(ax=ax, c='green')\n", + " explore_df['no_block'].plot(ax=ax, c='yellow')\n", + " exploit_df['knowledge'].plot(ax=ax, c='red')\n", + " ax.axvline(x=len(explore_df), c='black', linestyle='dashed')\n", + "\n", + " ax.set_title(\"Achieved knowledge\", fontsize=TITLE_TEXT_SIZE)\n", + " ax.set_xlabel(\"Trial\", fontsize=AXIS_TEXT_SIZE)\n", + " ax.set_ylabel(\"Knowledge [%]\", fontsize=AXIS_TEXT_SIZE)\n", + " ax.set_ylim([0, 105])\n", + " ax.legend(fontsize=LEGEND_TEXT_SIZE)\n", + "\n", + "def plot_classifiers(df, ax=None):\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " explore_df = df.query(\"phase == 'explore'\")\n", + " exploit_df = df.query(\"phase == 'exploit'\")\n", + "\n", + " df['numerosity'].plot(ax=ax, c='blue')\n", + " df['reliable'].plot(ax=ax, c='red')\n", + "\n", + " ax.axvline(x=len(explore_df), c='black', linestyle='dashed')\n", + "\n", + " ax.set_title(\"Classifiers\", fontsize=TITLE_TEXT_SIZE)\n", + " ax.set_xlabel(\"Trial\", fontsize=AXIS_TEXT_SIZE)\n", + " ax.set_ylabel(\"Classifiers\", fontsize=AXIS_TEXT_SIZE)\n", + " ax.legend(fontsize=LEGEND_TEXT_SIZE)\n", + "\n", + "\n", + "def plot_performance(agent, env, metrics_df, cfg, env_name, additional_info):\n", + " plt.figure(figsize=(13, 10), dpi=100)\n", + " plt.suptitle(f'ACS2 Performance in {env_name} environment '\n", + " f'{additional_info}', fontsize=32)\n", + "\n", + " # ax1 = plt.subplot(221)\n", + " # plot_policy(env, agent, cfg, ax1)\n", + "\n", + " ax2 = plt.subplot(211)\n", + " plot_knowledge(metrics_df, ax2)\n", + "\n", + " ax3 = plt.subplot(212)\n", + " plot_classifiers(metrics_df, ax3)\n", + "\n", + " # ax4 = plt.subplot(224)\n", + " # plot_steps(metrics_df, ax4)\n", + "\n", + " plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3)\n", + "\n", + "\n", + "def plot_handeye(env_name='HandEye3-v0', filename='images/handeye.pdf', do_action_planning=True):\n", + " hand_eye = gym.make(env_name)\n", + " cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n,\n", + " epsilon=1.0,\n", + " do_ga=False,\n", + " do_action_planning=do_action_planning,\n", + " performance_fcn=calculate_performance)\n", + "\n", + " # explore\n", + " agent_he = ACS2(cfg)\n", + " population_maze5_explore, metrics_maze5_explore = agent_he.explore(\n", + " hand_eye, 50)\n", + "\n", + " # exploit\n", + " agent_he = ACS2(cfg, population_maze5_explore)\n", + " _, metrics_maze5_exploit = agent_he.exploit(hand_eye, 10)\n", + "\n", + " maze5_metrics_df = parse_metrics_to_df(metrics_maze5_explore,\n", + " metrics_maze5_exploit)\n", + "\n", + " if do_action_planning:\n", + " message = 'with'\n", + " else:\n", + " message = 'without'\n", + "\n", + " plot_performance(agent_he, hand_eye, maze5_metrics_df, cfg, env_name,\n", + " '\\n{} Action Planning'.format(message))\n", + " plt.savefig(filename.replace(\" \", \"_\"), format='pdf', dpi=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, finally, we can use the above functions and plot the results with and without Action Planning. I decided to save the plots to pdf files to easily compare it to each other.\n", + "\n", + "It may take a while, so be patient. Usually, HandEye3-v0 environment takes about 3-4 minutes to complete. Bigger environments needs more time.\n", + "\n", + "Note: in 'plot_handeye' function the number '50' in agent_he.explore means 50 trials, 500 steps each. If you want to test bigger environments, you may need bigger number of trials to achieve 100.0% knowledge." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time start: 2018-09-26 15:54:44.317150\n", + "done with AP, time: 2018-09-26 15:56:27.589234, elapsed: 0:01:43.272084\n", + "done without AP, time: 2018-09-26 15:57:42.879115, elapsed: 0:01:15.289881\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "env_name = 'HandEye3-v0'\n", + "\n", + "start = datetime.datetime.now()\n", + "print(\"time start: {}\".format(start))\n", + "\n", + "plot_handeye(env_name, 'images/{}_ap.pdf'.format(env_name),\n", + " do_action_planning=True)\n", + "\n", + "middle = datetime.datetime.now()\n", + "print(\"done with AP, time: {}, elapsed: {}\".format(middle, middle-start))\n", + "\n", + "plot_handeye(env_name, 'images/{}_no_ap.pdf'.format(env_name),\n", + " do_action_planning=False)\n", + "\n", + "end = datetime.datetime.now()\n", + "print(\"done without AP, time: {}, elapsed: {}\".format(end, end-middle))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can compare results.\n", + "\n", + "Remember that each trial means 500 steps. In most of the results (not all because of non-deterministic nature of ACS2), we can see that when using Action Planning, we achieve full knowledge (100.0%) about 6-8 trials faster (sometimes more, sometimes less than that). It may seem like a really small difference, but it is actually about 3000-4000 steps.\n", + "\n", + "Overall, Action Planning makes a difference, a rather big difference, but not as big as in the article mentioned at the beginning." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/acs2/handeye/images/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf b/docs/source/notebooks/handeye/images/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf similarity index 100% rename from examples/acs2/handeye/images/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf rename to docs/source/notebooks/handeye/images/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf diff --git a/examples/acs2/handeye/images/HandEye2-v0_no_ap_2018-09-21 11:25:11.446657.pdf b/docs/source/notebooks/handeye/images/HandEye2-v0_no_ap_2018-09-21 11:25:11.446657.pdf similarity index 100% rename from examples/acs2/handeye/images/HandEye2-v0_no_ap_2018-09-21 11:25:11.446657.pdf rename to docs/source/notebooks/handeye/images/HandEye2-v0_no_ap_2018-09-21 11:25:11.446657.pdf diff --git a/examples/acs2/handeye/images/HandEye2_ap_2018-09-20 16:01:56.258596.pdf b/docs/source/notebooks/handeye/images/HandEye2_ap_2018-09-20 16:01:56.258596.pdf similarity index 100% rename from examples/acs2/handeye/images/HandEye2_ap_2018-09-20 16:01:56.258596.pdf rename to docs/source/notebooks/handeye/images/HandEye2_ap_2018-09-20 16:01:56.258596.pdf diff --git a/examples/acs2/handeye/images/HandEye2_no_ap_2018-09-20 16:01:56.258596.pdf b/docs/source/notebooks/handeye/images/HandEye2_no_ap_2018-09-20 16:01:56.258596.pdf similarity index 100% rename from examples/acs2/handeye/images/HandEye2_no_ap_2018-09-20 16:01:56.258596.pdf rename to docs/source/notebooks/handeye/images/HandEye2_no_ap_2018-09-20 16:01:56.258596.pdf diff --git a/examples/acs2/handeye/images/HandEye3-v0_ap_2018-09-21 11:27:24.223319.pdf b/docs/source/notebooks/handeye/images/HandEye3-v0_ap_2018-09-21 11:27:24.223319.pdf similarity index 100% rename from examples/acs2/handeye/images/HandEye3-v0_ap_2018-09-21 11:27:24.223319.pdf rename to docs/source/notebooks/handeye/images/HandEye3-v0_ap_2018-09-21 11:27:24.223319.pdf diff --git a/examples/acs2/handeye/images/HandEye3-v0_ap_2018-09-21 11:41:38.637807.pdf b/docs/source/notebooks/handeye/images/HandEye3-v0_ap_2018-09-21 11:41:38.637807.pdf similarity index 100% rename from examples/acs2/handeye/images/HandEye3-v0_ap_2018-09-21 11:41:38.637807.pdf rename to docs/source/notebooks/handeye/images/HandEye3-v0_ap_2018-09-21 11:41:38.637807.pdf diff --git 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docs/source/notebooks/handeye/plot_handeye.py index 6e50ec2b..b4f70a0f 100644 --- a/examples/acs2/handeye/plot_handeye.py +++ b/docs/source/notebooks/handeye/plot_handeye.py @@ -136,7 +136,7 @@ def plot_handeye(env_name='HandEye3-v0', filename='images/handeye.pdf', do_actio plot_performance(agent_he, hand_eye, maze5_metrics_df, cfg, env_name, '\n{} Action Planning'.format(message)) - plt.savefig(filename, format='pdf', dpi=100) + plt.savefig(filename.replace(" ", "_"), format='pdf', dpi=100) if __name__ == "__main__": From cb7ed2648a95e47b141b240c926f58b018dcdc30 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Thu, 27 Sep 2018 12:07:05 +0200 Subject: [PATCH 59/83] added notebook --- .../notebooks/handeye/ACS2_in_HandEye.ipynb | 188 +++++++++++++++--- docs/source/notebooks/handeye/plot_handeye.py | 9 +- .../notebooks/handeye/results_handeye.py | 0 examples/acs2/handeye/acs2_in_handeye_ap.py | 1 + lcs/agents/acs2/ACS2.py | 1 - 5 files changed, 167 insertions(+), 32 deletions(-) create mode 100644 docs/source/notebooks/handeye/results_handeye.py diff --git a/docs/source/notebooks/handeye/ACS2_in_HandEye.ipynb b/docs/source/notebooks/handeye/ACS2_in_HandEye.ipynb index 5c8d06c7..bb62a0b3 100644 --- a/docs/source/notebooks/handeye/ACS2_in_HandEye.ipynb +++ b/docs/source/notebooks/handeye/ACS2_in_HandEye.ipynb @@ -5,18 +5,18 @@ "metadata": {}, "source": [ "# ACS2 in HandEye environment with Action Planning\n", - "This notebook presents how to use HandEye environment with and without Action Planning.\n", + "This notebook presents how to use HandEye environment and how it works with and without Action Planning.\n", "\n", "All of ACS2 code can be found in a Github repository: https://github.com/ParrotPrediction/pyalcs. \n", "\n", - "If you want to read more about HandEye envronment, it is described in an article by Martin Butz and Wolfgang Stolzmann: 'Action-Planning in Anticipatory Classifier Systems'. It also describes how Action Planning works.\n", + "If you want to read more about HandEye envronment, it is described in an article by Martin Butz and Wolfgang Stolzmann: 'Action-Planning in Anticipatory Classifier Systems'. It also describes how Action Planning works. In this article's results Action Planning makes gaining the knowledge about the environment about two times faster than without Action Planning. Let's see how Action Planning works in pyALCS in HandEye environment.\n", "\n", - "First, we need to import important dependencies." + "First, we need to import dependencies." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -37,25 +37,123 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[32m#\u001b[0m\u001b[34m■\u001b[0m\u001b[37m□\u001b[0m\n", + "\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\n", + "\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\n" + ] + } + ], + "source": [ + "hand_eye = gym.make('HandEye3-v0')\n", + "\n", + "state = hand_eye.reset()\n", + "\n", + "hand_eye.render()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the rendered state above, color white is for surface, green is for gripper, and blue is for block.\n", + "\n", + "The number after the name of the environment (in this example - '3') stands for the size of the environment. You can use environments of size from 2 up to 9, ex. 'HandEye5-v0' is an environment of size 5.\n", + "\n", + "We can perform actions on the environment. There are 6 actions. 0 - move North, 1 - move East, 2 - move South, 3 - move West, 4 - grip the block, 5 - release the block." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[37m□\u001b[0m\u001b[34m■\u001b[0m\u001b[37m□\u001b[0m\n", + "\u001b[32m#\u001b[0m\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\n", + "\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\n" + ] + } + ], + "source": [ + "action = 2 # move South\n", + "\n", + "state, reward, done, _ = hand_eye.step(action)\n", + "\n", + "hand_eye.render()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also display state of the environment as a tuple, line by line. 'w' stands for the surface, 'b' is for block and 'g' is for gripper. The last element is the feeling sensor of the gripper. '0' means no feel of block, '1' means the block is under the gripper, and '2' means the block is in hand." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('w', 'b', 'w', 'g', 'w', 'w', 'w', 'w', 'w', '0')\n" + ] + } + ], "source": [ - "hand_eye = gym.make('HandEye3-v0')" + "print(state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The number after the name of the environment (in this example - '3') stands for the size of the environment. You can use environments of size from 2 up to 9.\n", + "In this environment, the reward is always 0, because there is no goal. Therefore, it is harder to learn the good moves." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "print(reward)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The agent\n", "\n", "After creating the environment, we need to configure the agent." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -63,6 +161,7 @@ " epsilon=1.0,\n", " do_ga=False,\n", " do_action_planning=True,\n", + " action_planning_frequency=50,\n", " performance_fcn=calculate_performance)" ] }, @@ -70,9 +169,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As you can see, we set the 'do_action_planning' variable to True. You can of course use this environment without Action Plannin by simply setting this variable to False.\n", + "As you can see, we set the 'do_action_planning' variable to True. You can of course use this environment without Action Planning by simply setting this variable to False.\n", "\n", - "Now we can just create the ACS2 agent to explore the environment:" + "'action_planning_frequency' sets the frequency of the Action Planning phase, meaning that every 50 steps the agent will enter the Action Planning phase.\n", + "\n", + "Now we can just create the ACS2 agent to explore the environment. I set the number of trials in the explore phase to 5 and in the exploit phase to 1. Each trial has up to 500 steps (usually exactly 500)." ] }, { @@ -83,20 +184,55 @@ "source": [ " # Explore the environment\n", "agent = ACS2(cfg)\n", - "population, explore_metrics = agent.explore(hand_eye, 50)\n", + "population, explore_metrics = agent.explore(hand_eye, 5)\n", "\n", "# Exploit the environment\n", "agent = ACS2(cfg, population)\n", - "population, exploit_metric = agent.exploit(hand_eye, 10)" + "population, exploit_metric = agent.exploit(hand_eye, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating the environment and running an agent in it is really simple." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Creating the environment and running an agent in it is really simple and can look really spectacular, but that's not the most interesting part.\n", + "## Action Planning\n", + "\n", + "In this section, I will briefly explain how the Action Planning phase works.\n", + "\n", + "Every 50 steps, the Action Planning phase takes place. \n", + "\n", + "Firstly, it starts with getting the goal state from the environment's goal-generator - the function 'get_goal_state'. Not every environment has it. Action Planning won't work in an environment without the function 'get_goal_state'.\n", "\n", - "## Graphs\n", + "If it has the goal, it starts searching for the sequence of moves to change current state into the goal state. It is done by a bidirectional search algorithm using only reliable classifiers. It there are no reliable classifiers to anticipate the change from the current state to the goal state, the goal sequence is not found and the agent goes back to the exploration phase. Notice that when the agent doesn't have full knowledge about the environment, it may find a sequence that is longer than the shortest sequence.\n", + "\n", + "Once the goal sequence is found, the agent executes each action and learns during executing. Then it asks the environment for the next goal. It is done as long as there are new goals and it can find a sequence of moves from a current state to a goal state.\n", + "\n", + "### Action Planning in HandEye environment\n", + "\n", + "The Action Planning phase is really helpful in the environments like HandEye - without one specific goal to reach. \n", + "\n", + "The goal generator of the HandEye environment generates continuously the following states:\n", + " 1. Move over the block.\n", + " 2. Grip the block.\n", + " 3. Move with the block to a random position.\n", + " 4. Release the block.\n", + " 5. Move to a random position not over the block.\n", + "\n", + "In the normal exploration phase, the environment is rarely in the position with block in hand. In Action Planning phase, however, it has to continously find the block, move over it, grip it and move it elsewhere. There are a lot less actions with block than without block. Even in 2x2 environment, actions with block take only 32% of all possible actions and for bigger environments this percentage is even smaller (ex. 3x3 - 16%, 7x7 - 3.1%)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Results\n", "\n", "If we want to compare the result from the HandEye environment with and without Action Planning, the most convenient way is to use graphs. Of course the ACS2 agent is not deterministic, but we won't worry about it now.\n", "\n", @@ -219,22 +355,17 @@ " plt.suptitle(f'ACS2 Performance in {env_name} environment '\n", " f'{additional_info}', fontsize=32)\n", "\n", - " # ax1 = plt.subplot(221)\n", - " # plot_policy(env, agent, cfg, ax1)\n", - "\n", " ax2 = plt.subplot(211)\n", " plot_knowledge(metrics_df, ax2)\n", "\n", " ax3 = plt.subplot(212)\n", " plot_classifiers(metrics_df, ax3)\n", "\n", - " # ax4 = plt.subplot(224)\n", - " # plot_steps(metrics_df, ax4)\n", - "\n", " plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3)\n", "\n", "\n", - "def plot_handeye(env_name='HandEye3-v0', filename='images/handeye.pdf', do_action_planning=True):\n", + "def plot_handeye(env_name='HandEye3-v0', filename='images/handeye.pdf', \n", + " do_action_planning=True):\n", " hand_eye = gym.make(env_name)\n", " cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n,\n", " epsilon=1.0,\n", @@ -270,7 +401,7 @@ "source": [ "Then, finally, we can use the above functions and plot the results with and without Action Planning. I decided to save the plots to pdf files to easily compare it to each other.\n", "\n", - "It may take a while, so be patient. Usually, HandEye3-v0 environment takes about 3-4 minutes to complete. Bigger environments needs more time.\n", + "It may take a while, so be patient. Usually, HandEye3-v0 environment takes about 3-4 minutes to complete. Bigger environments need more time.\n", "\n", "Note: in 'plot_handeye' function the number '50' in agent_he.explore means 50 trials, 500 steps each. If you want to test bigger environments, you may need bigger number of trials to achieve 100.0% knowledge." ] @@ -343,7 +474,16 @@ "\n", "Remember that each trial means 500 steps. In most of the results (not all because of non-deterministic nature of ACS2), we can see that when using Action Planning, we achieve full knowledge (100.0%) about 6-8 trials faster (sometimes more, sometimes less than that). It may seem like a really small difference, but it is actually about 3000-4000 steps.\n", "\n", - "Overall, Action Planning makes a difference, a rather big difference, but not as big as in the article mentioned at the beginning." + "What is even more important, we achieve knowledge about transitions with block faster than without Action Planning. This is essential, because moves with block are more useful for the real use of the environment.\n", + "\n", + "Of course, this is only one sample of a non-deterministic agent. The results shown above are not 'rules'. They are not in any way relevant and do not show even typical behaviour of pyALCS with Action Planning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" ] } ], diff --git a/docs/source/notebooks/handeye/plot_handeye.py b/docs/source/notebooks/handeye/plot_handeye.py index b4f70a0f..3cab20dc 100644 --- a/docs/source/notebooks/handeye/plot_handeye.py +++ b/docs/source/notebooks/handeye/plot_handeye.py @@ -94,22 +94,17 @@ def plot_performance(agent, env, metrics_df, cfg, env_name, additional_info): plt.suptitle(f'ACS2 Performance in {env_name} environment ' f'{additional_info}', fontsize=32) - # ax1 = plt.subplot(221) - # plot_policy(env, agent, cfg, ax1) - ax2 = plt.subplot(211) plot_knowledge(metrics_df, ax2) ax3 = plt.subplot(212) plot_classifiers(metrics_df, ax3) - # ax4 = plt.subplot(224) - # plot_steps(metrics_df, ax4) - plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3) -def plot_handeye(env_name='HandEye3-v0', filename='images/handeye.pdf', do_action_planning=True): +def plot_handeye(env_name='HandEye3-v0', filename='images/handeye.pdf', + do_action_planning=True): hand_eye = gym.make(env_name) cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, epsilon=1.0, diff --git a/docs/source/notebooks/handeye/results_handeye.py b/docs/source/notebooks/handeye/results_handeye.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/acs2/handeye/acs2_in_handeye_ap.py b/examples/acs2/handeye/acs2_in_handeye_ap.py index a28d17fd..e7784fae 100644 --- a/examples/acs2/handeye/acs2_in_handeye_ap.py +++ b/examples/acs2/handeye/acs2_in_handeye_ap.py @@ -19,6 +19,7 @@ epsilon=1.0, do_ga=False, do_action_planning=True, + action_planning_frequency=50, performance_fcn=calculate_performance) logging.info(cfg) diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index f135a860..0a5eac34 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -244,7 +244,6 @@ def _run_trial_exploit(self, env, time=None, current_trial=None): return steps - def _run_action_planning(self, env, time: int, state: str, From 1393451b20940e12f50fc474bf76f3de30257ad5 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Thu, 27 Sep 2018 14:39:23 +0200 Subject: [PATCH 60/83] result_handeye works --- .../notebooks/handeye/results_handeye.py | 189 ++++++++++++++++++ 1 file changed, 189 insertions(+) diff --git a/docs/source/notebooks/handeye/results_handeye.py b/docs/source/notebooks/handeye/results_handeye.py index e69de29b..8c2ab065 100644 --- a/docs/source/notebooks/handeye/results_handeye.py +++ b/docs/source/notebooks/handeye/results_handeye.py @@ -0,0 +1,189 @@ +# Plot constants +import datetime + +import gym +import gym_handeye + +from lcs.agents.acs2 import ACS2, ClassifiersList, Configuration +import numpy as np +import pandas as pd +import matplotlib +import matplotlib.pyplot as plt + +from examples.acs2.handeye.utils import calculate_performance + +TITLE_TEXT_SIZE=24 +AXIS_TEXT_SIZE=18 +LEGEND_TEXT_SIZE=16 + + +def parse_metrics_to_df(explore_metrics, exploit_metrics): + def extract_details(row): + row['trial'] = row['agent']['trial'] + row['steps'] = row['agent']['steps'] + row['numerosity'] = row['agent']['numerosity'] + row['reliable'] = row['agent']['reliable'] + row['knowledge'] = row['performance']['knowledge'] + row['with_block'] = row['performance']['with_block'] + row['no_block'] = row['performance']['no_block'] + return row + + # Load both metrics into data frame + explore_df = pd.DataFrame(explore_metrics) + exploit_df = pd.DataFrame(exploit_metrics) + + # Mark them with specific phase + explore_df['phase'] = 'explore' + exploit_df['phase'] = 'exploit' + + # Extract details + explore_df = explore_df.apply(extract_details, axis=1) + exploit_df = exploit_df.apply(extract_details, axis=1) + + # Adjuts exploit trial counter + exploit_df['trial'] = exploit_df.apply( + lambda r: r['trial'] + len(explore_df), axis=1) + + # Concatenate both dataframes + df = pd.concat([explore_df, exploit_df]) + df.drop(['agent', 'environment', 'performance'], axis=1, inplace=True) + df.set_index('trial', inplace=True) + + return df + + +def plot_knowledge(df, ax=None): + if ax is None: + ax = plt.gca() + + explore_df = df.query("phase == 'explore'") + exploit_df = df.query("phase == 'exploit'") + + explore_df['knowledge'].plot(ax=ax, c='blue') + explore_df['with_block'].plot(ax=ax, c='green') + explore_df['no_block'].plot(ax=ax, c='yellow') + exploit_df['knowledge'].plot(ax=ax, c='red') + ax.axvline(x=len(explore_df), c='black', linestyle='dashed') + + ax.set_title("Achieved knowledge", fontsize=TITLE_TEXT_SIZE) + ax.set_xlabel("Trial", fontsize=AXIS_TEXT_SIZE) + ax.set_ylabel("Knowledge [%]", fontsize=AXIS_TEXT_SIZE) + ax.set_ylim([0, 105]) + ax.legend(fontsize=LEGEND_TEXT_SIZE) + + +def plot_classifiers(df, ax=None): + if ax is None: + ax = plt.gca() + + explore_df = df.query("phase == 'explore'") + exploit_df = df.query("phase == 'exploit'") + + df['numerosity'].plot(ax=ax, c='blue') + df['reliable'].plot(ax=ax, c='red') + + ax.axvline(x=len(explore_df), c='black', linestyle='dashed') + + ax.set_title("Classifiers", fontsize=TITLE_TEXT_SIZE) + ax.set_xlabel("Trial", fontsize=AXIS_TEXT_SIZE) + ax.set_ylabel("Classifiers", fontsize=AXIS_TEXT_SIZE) + ax.legend(fontsize=LEGEND_TEXT_SIZE) + + +def plot_performance(metrics_df, env_name, additional_info): + plt.figure(figsize=(13, 10), dpi=100) + plt.suptitle(f'ACS2 Performance in {env_name} environment ' + f'{additional_info}', fontsize=32) + + ax2 = plt.subplot(211) + plot_knowledge(metrics_df, ax2) + + ax3 = plt.subplot(212) + plot_classifiers(metrics_df, ax3) + + plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3) + + +def mean(i, row_mean, row, first, second): + return (row_mean[first][second] + * i + row[first][second]) / (i + 1) + + +def count_mean_values(i: int, metrics, mean_metrics): + new_metrics = metrics.copy() + for row, row_new, row_mean in zip(metrics, new_metrics, mean_metrics): + row_new['performance']['knowledge'] = mean(i, row_mean, row, + 'performance', 'knowledge') + row_new['performance']['with_block'] = mean(i, row_mean, row, + 'performance', + 'with_block') + row_new['performance']['no_block'] = mean(i, row_mean, row, + 'performance', 'no_block') + row_new['agent']['numerosity'] = mean(i, row_mean, row, + 'agent', 'numerosity') + row_new['agent']['steps'] = mean(i, row_mean, row, + 'agent', 'steps') + row_new['agent']['reliable'] = mean(i, row_mean, row, + 'agent', 'reliable') + return new_metrics + + +def plot_handeye(env_name='HandEye3-v0', filename='images/handeye.pdf', + do_action_planning=True): + hand_eye = gym.make(env_name) + cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, + epsilon=1.0, + do_ga=False, + do_action_planning=do_action_planning, + performance_fcn=calculate_performance) + + mean_metrics_he_exploit = [] + mean_metrics_he_explore = [] + + agent_he = ACS2(cfg) + + for i in range(50): + # explore + agent_he = ACS2(cfg) + population_he_explore, metrics_he_explore = agent_he.explore( + hand_eye, 14) + + # exploit + agent_he = ACS2(cfg, population_he_explore) + _, metrics_he_exploit = agent_he.exploit(hand_eye, 2) + + mean_metrics_he_explore = count_mean_values(i, metrics_he_explore, + mean_metrics_he_explore) + mean_metrics_he_exploit = count_mean_values(i, metrics_he_exploit, + mean_metrics_he_exploit) + + he_metrics_df = parse_metrics_to_df(mean_metrics_he_explore, + mean_metrics_he_exploit) + + if do_action_planning: + message = 'with' + else: + message = 'without' + + plot_performance(he_metrics_df, env_name, + '\n{} Action Planning'.format(message)) + plt.savefig(filename.replace(" ", "_"), format='pdf', dpi=100) + + +if __name__ == "__main__": + env_name = 'HandEye2-v0' + + start = datetime.datetime.now() + print("time start: {}".format(start)) + + plot_handeye(env_name, 'plots/{}_ap_{}.pdf'.format(env_name, start), + do_action_planning=True) + + middle = datetime.datetime.now() + print("done with AP, time: {}, elapsed: {}".format(middle, middle-start)) + + plot_handeye(env_name, 'plots/{}_no_ap_{}.pdf'.format(env_name, start), + do_action_planning=False) + + end = datetime.datetime.now() + print("done without AP, time: {}, elapsed: {}".format(end, end-middle)) From 5908f97c657bbfa3e13be9c1e2d41688582210ad Mon Sep 17 00:00:00 2001 From: e-dzia Date: Thu, 27 Sep 2018 15:53:06 +0200 Subject: [PATCH 61/83] results_handeye makes plots for both ap and no ap --- ...dEye2-v0_ap_2018-09-27_15:27:16.955187.pdf | Bin 0 -> 17227 bytes ...ye2-v0_both_2018-09-27_15:27:16.955187.pdf | Bin 0 -> 16194 bytes ...e2-v0_no_ap_2018-09-27_15:27:16.955187.pdf | Bin 0 -> 17223 bytes ...dEye3-v0_ap_2018-09-27_13:32:50.033514.pdf | Bin 0 -> 17474 bytes ...e3-v0_no_ap_2018-09-27_13:32:50.033514.pdf | Bin 0 -> 17121 bytes .../notebooks/handeye/results_handeye.py | 49 +++++++++++++----- 6 files changed, 35 insertions(+), 14 deletions(-) create mode 100644 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z8X7p3tuzz@$Q=6n!!rUbeZXQj(ZEg&nkidoxNT@i;J3Gw!{Gm+AyA^=0`}%|2oxUN z4&OpUqkuozO2a^lBJ}qk&uHv6@7&L(UTWQ#T<$@9YSAJL|@Ksyt zVxcp`tuzdXSlLR0z}OZV4zUdl4gBQRau8P9LK8v!D+3^cf5t0<`^VN0z>WUXhbRK^ zk6c8N|JD_SeDxMyL@~fGZl#GpYdrM#pWZ|v9I%CkNByfKJO5wL;$Q`d`N;oxjTSpQcPs;;)cr2(ZvPF&Z`4O)lS^Lh>eHeexx a7B^2X3xe1B(uTx};*jJ#Jc?RM Date: Thu, 27 Sep 2018 15:54:35 +0200 Subject: [PATCH 62/83] results_handeye makes plots for both ap and no ap --- docs/source/notebooks/handeye/results_handeye.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/notebooks/handeye/results_handeye.py b/docs/source/notebooks/handeye/results_handeye.py index 42c54e71..6745d6e5 100644 --- a/docs/source/notebooks/handeye/results_handeye.py +++ b/docs/source/notebooks/handeye/results_handeye.py @@ -159,7 +159,7 @@ def plot_handeye(number_of_tests=50, env_name='HandEye3-v0', filename='images/ha # explore agent_he = ACS2(cfg) population_he_explore, metrics_he_explore = agent_he.explore( - hand_eye, 14) + hand_eye, 50) # exploit agent_he = ACS2(cfg, population_he_explore) From 6ef142237b24e151b45c655a2a4d0e6132c4f907 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Thu, 27 Sep 2018 15:59:54 +0200 Subject: [PATCH 63/83] line was too long --- docs/source/notebooks/handeye/results_handeye.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/source/notebooks/handeye/results_handeye.py b/docs/source/notebooks/handeye/results_handeye.py index 6745d6e5..0eaeabed 100644 --- a/docs/source/notebooks/handeye/results_handeye.py +++ b/docs/source/notebooks/handeye/results_handeye.py @@ -199,7 +199,8 @@ def plot_with_without_ap(filename, metrics_ap, metrics_no_ap): middle = datetime.datetime.now() print("done with AP, time: {}, elapsed: {}".format(middle, middle-start)) - metrics_no_ap = plot_handeye(number_of_tests, env_name, 'plots/{}_no_ap_{}.pdf'. + metrics_no_ap = plot_handeye(number_of_tests, env_name, + 'plots/{}_no_ap_{}.pdf'. format(env_name, start), do_action_planning=False) From f42a32304aba70b1d37ed227510ad8c9fbdcc0cb Mon Sep 17 00:00:00 2001 From: e-dzia Date: Fri, 28 Sep 2018 11:25:05 +0200 Subject: [PATCH 64/83] ready to test --- docs/source/notebooks/handeye/results_handeye.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/notebooks/handeye/results_handeye.py b/docs/source/notebooks/handeye/results_handeye.py index 0eaeabed..c07d4ac0 100644 --- a/docs/source/notebooks/handeye/results_handeye.py +++ b/docs/source/notebooks/handeye/results_handeye.py @@ -186,8 +186,8 @@ def plot_with_without_ap(filename, metrics_ap, metrics_no_ap): if __name__ == "__main__": - env_name = 'HandEye2-v0' - number_of_tests = 50 + env_name = 'HandEye3-v0' + number_of_tests = 100 start = datetime.datetime.now() print("time start: {}".format(start)) From 58058e63357ba5d714e3f47d94b9f56d47d8205b Mon Sep 17 00:00:00 2001 From: e-dzia Date: Tue, 2 Oct 2018 17:51:28 +0200 Subject: [PATCH 65/83] changed titles in results_handeye --- ...ye3-v0_ap_2_2018-10-02_17:12:35.570470.pdf | Bin 0 -> 18822 bytes ...ye3-v0_ap_2_2018-10-02_17:28:30.974485.pdf | Bin 0 -> 19443 bytes ...ye3-v0_ap_2_2018-10-02_17:41:51.321173.pdf | Bin 0 -> 19480 bytes ...3-v0_both_2_2018-10-02_17:12:35.570470.pdf | Bin 0 -> 18665 bytes ...3-v0_both_2_2018-10-02_17:28:30.974485.pdf | Bin 0 -> 19563 bytes ...3-v0_both_2_2018-10-02_17:41:51.321173.pdf | Bin 0 -> 19775 bytes ...-v0_no_ap_2_2018-10-02_17:12:35.570470.pdf | Bin 0 -> 18928 bytes ...-v0_no_ap_2_2018-10-02_17:28:30.974485.pdf | Bin 0 -> 19638 bytes ...-v0_no_ap_2_2018-10-02_17:41:51.321173.pdf | Bin 0 -> 19615 bytes .../notebooks/handeye/results_handeye.py | 63 ++++++++++++------ 10 files changed, 42 insertions(+), 21 deletions(-) create mode 100644 docs/source/notebooks/handeye/plots/HandEye3-v0_ap_2_2018-10-02_17:12:35.570470.pdf create mode 100644 docs/source/notebooks/handeye/plots/HandEye3-v0_ap_2_2018-10-02_17:28:30.974485.pdf create mode 100644 docs/source/notebooks/handeye/plots/HandEye3-v0_ap_2_2018-10-02_17:41:51.321173.pdf create mode 100644 docs/source/notebooks/handeye/plots/HandEye3-v0_both_2_2018-10-02_17:12:35.570470.pdf create mode 100644 docs/source/notebooks/handeye/plots/HandEye3-v0_both_2_2018-10-02_17:28:30.974485.pdf create mode 100644 docs/source/notebooks/handeye/plots/HandEye3-v0_both_2_2018-10-02_17:41:51.321173.pdf create mode 100644 docs/source/notebooks/handeye/plots/HandEye3-v0_no_ap_2_2018-10-02_17:12:35.570470.pdf create mode 100644 docs/source/notebooks/handeye/plots/HandEye3-v0_no_ap_2_2018-10-02_17:28:30.974485.pdf create mode 100644 docs/source/notebooks/handeye/plots/HandEye3-v0_no_ap_2_2018-10-02_17:41:51.321173.pdf diff --git a/docs/source/notebooks/handeye/plots/HandEye3-v0_ap_2_2018-10-02_17:12:35.570470.pdf b/docs/source/notebooks/handeye/plots/HandEye3-v0_ap_2_2018-10-02_17:12:35.570470.pdf new file mode 100644 index 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00:00:00 2001 From: e-dzia Date: Wed, 3 Oct 2018 10:16:12 +0200 Subject: [PATCH 67/83] rename results_handeye --- .../handeye/{results_handeye.py => mean_results_handeye.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename docs/source/notebooks/handeye/{results_handeye.py => mean_results_handeye.py} (100%) diff --git a/docs/source/notebooks/handeye/results_handeye.py b/docs/source/notebooks/handeye/mean_results_handeye.py similarity index 100% rename from docs/source/notebooks/handeye/results_handeye.py rename to docs/source/notebooks/handeye/mean_results_handeye.py From de86fee64a1943421a5efe01f9ae6de635d6b927 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 3 Oct 2018 10:20:48 +0200 Subject: [PATCH 68/83] renamed directories --- .../HandEye2-v0_ap_2018-09-27_15:27:16.955187.pdf | Bin .../HandEye2-v0_both_2018-09-27_15:27:16.955187.pdf | Bin ...HandEye2-v0_no_ap_2018-09-27_15:27:16.955187.pdf | Bin ...andEye3-v0_ap_100_2018-10-02_23:02:51.492298.pdf | Bin 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docs/source/notebooks/handeye/mean_results/HandEye3-v0_no_ap_2_2018-10-02_17:41:51.321173.pdf diff --git a/docs/source/notebooks/handeye/mean_results_handeye.py b/docs/source/notebooks/handeye/mean_results_handeye.py index 724ae899..10bb3772 100644 --- a/docs/source/notebooks/handeye/mean_results_handeye.py +++ b/docs/source/notebooks/handeye/mean_results_handeye.py @@ -91,7 +91,7 @@ def plot_classifiers(df, ax=None): def plot_performance(metrics_df, env_name, additional_info, - with_AP=", with Action Planning"): + with_AP=""): plt.figure(figsize=(13, 10), dpi=100) plt.suptitle(f'ACS2 Performance in {env_name} environment ' f'{additional_info}', fontsize=32) @@ -145,7 +145,7 @@ def count_mean_values(i: int, metrics, mean_metrics): def plot_handeye_mean(number_of_tests=50, env_name='HandEye3-v0', - filename='images/handeye.pdf', do_action_planning=True, + filename='mean_results/handeye.pdf', do_action_planning=True, number_of_trials_explore=50, number_of_trials_exploit=2): hand_eye = gym.make(env_name) cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, @@ -203,7 +203,7 @@ def plot_with_without_ap(filename, metrics_ap, metrics_no_ap): print("time start: {}".format(start)) metrics_ap = plot_handeye_mean(number_of_tests, env_name, - 'plots/{}_ap_{}_{}.pdf'. + 'mean_results/{}_ap_{}_{}.pdf'. format(env_name, number_of_tests, start), do_action_planning=True, number_of_trials_explore= @@ -215,7 +215,7 @@ def plot_with_without_ap(filename, metrics_ap, metrics_no_ap): print("done with AP, time: {}, elapsed: {}".format(middle, middle-start)) metrics_no_ap = plot_handeye_mean(number_of_tests, env_name, - 'plots/{}_no_ap_{}_{}.pdf'. + 'mean_results/{}_no_ap_{}_{}.pdf'. format(env_name, number_of_tests, start), do_action_planning=False, number_of_trials_explore= @@ -226,7 +226,7 @@ def plot_with_without_ap(filename, metrics_ap, metrics_no_ap): end = datetime.datetime.now() print("done without AP, time: {}, elapsed: {}".format(end, end-middle)) - plot_with_without_ap('plots/{}_both_{}_{}.pdf'.format(env_name, + plot_with_without_ap('mean_results/{}_both_{}_{}.pdf'.format(env_name, number_of_tests, start), metrics_ap, metrics_no_ap) diff --git a/docs/source/notebooks/handeye/plot_handeye.py b/docs/source/notebooks/handeye/plot_handeye.py index 3cab20dc..e6e05065 100644 --- a/docs/source/notebooks/handeye/plot_handeye.py +++ b/docs/source/notebooks/handeye/plot_handeye.py @@ -103,7 +103,7 @@ def plot_performance(agent, env, metrics_df, cfg, env_name, additional_info): plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3) -def plot_handeye(env_name='HandEye3-v0', filename='images/handeye.pdf', +def plot_handeye(env_name='HandEye3-v0', filename='results/handeye.pdf', do_action_planning=True): hand_eye = gym.make(env_name) cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, @@ -140,13 +140,13 @@ def plot_handeye(env_name='HandEye3-v0', filename='images/handeye.pdf', start = datetime.datetime.now() print("time start: {}".format(start)) - plot_handeye(env_name, 'images/{}_ap_{}.pdf'.format(env_name, start), + plot_handeye(env_name, 'results/{}_ap_{}.pdf'.format(env_name, start), do_action_planning=True) middle = datetime.datetime.now() print("done with AP, time: {}, elapsed: {}".format(middle, middle-start)) - plot_handeye(env_name, 'images/{}_no_ap_{}.pdf'.format(env_name, start), + plot_handeye(env_name, 'results/{}_no_ap_{}.pdf'.format(env_name, start), do_action_planning=False) end = datetime.datetime.now() diff --git a/docs/source/notebooks/handeye/images/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf b/docs/source/notebooks/handeye/results/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf similarity index 100% rename from docs/source/notebooks/handeye/images/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf rename to docs/source/notebooks/handeye/results/HandEye2-v0_ap_2018-09-21 11:25:11.446657.pdf diff --git a/docs/source/notebooks/handeye/images/HandEye2-v0_no_ap_2018-09-21 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11:30:55 +0200 Subject: [PATCH 69/83] added notebook with 100 experiments --- ...ACS2 in HandEye with Action Planning.ipynb | 445 ++++++++++++++++++ ...in_HandEye.ipynb => ACS2 in HandEye.ipynb} | 0 ...e3-v0_ap_2_2018-10-03_11:05:45.593276.pdf} | Bin 17706 -> 19584 bytes ...-v0_both_2_2018-10-03_11:05:45.593276.pdf} | Bin 18622 -> 19631 bytes ...e3-v0_no_ap_2018-09-27_13:32:50.033514.pdf | Bin 17121 -> 0 bytes ...v0_no_ap_2_2018-10-03_11:05:45.593276.pdf} | Bin 18969 -> 19544 bytes ...Eye3-v0_ap_2018-10-03_10:23:26.574512.pdf} | Bin 17474 -> 18853 bytes ...3-v0_no_ap_2018-10-03_10:23:26.574512.pdf} | Bin 17892 -> 18890 bytes ...dEye3_no_ap_2018-09-20 16:04:37.462759.pdf | Bin 18778 -> 0 bytes .../notebooks/handeye/results/handeye.pdf | Bin 18660 -> 0 bytes .../handeye_ap_2018-09-20 15:51:58.908574.pdf | Bin 18578 -> 0 bytes .../handeye/results/handeye_no_ap.pdf | Bin 18730 -> 0 bytes ...ndeye_no_ap_2018-09-20 15:51:58.908574.pdf | Bin 18615 -> 0 bytes 13 files changed, 445 insertions(+) create mode 100644 docs/source/notebooks/handeye/ACS2 in HandEye with Action Planning.ipynb rename docs/source/notebooks/handeye/{ACS2_in_HandEye.ipynb => ACS2 in HandEye.ipynb} (100%) rename docs/source/notebooks/handeye/{results/HandEye2_ap_2018-09-20 16:01:56.258596.pdf => mean_results/HandEye3-v0_ap_2_2018-10-03_11:05:45.593276.pdf} (67%) rename docs/source/notebooks/handeye/{results/handeye_ap.pdf => mean_results/HandEye3-v0_both_2_2018-10-03_11:05:45.593276.pdf} (67%) delete mode 100644 docs/source/notebooks/handeye/mean_results/HandEye3-v0_no_ap_2018-09-27_13:32:50.033514.pdf rename docs/source/notebooks/handeye/{results/HandEye3_ap_2018-09-20 16:04:37.462759.pdf => mean_results/HandEye3-v0_no_ap_2_2018-10-03_11:05:45.593276.pdf} (68%) rename docs/source/notebooks/handeye/{mean_results/HandEye3-v0_ap_2018-09-27_13:32:50.033514.pdf => results/HandEye3-v0_ap_2018-10-03_10:23:26.574512.pdf} (74%) rename docs/source/notebooks/handeye/results/{HandEye2_no_ap_2018-09-20 16:01:56.258596.pdf => HandEye3-v0_no_ap_2018-10-03_10:23:26.574512.pdf} (70%) delete mode 100644 docs/source/notebooks/handeye/results/HandEye3_no_ap_2018-09-20 16:04:37.462759.pdf delete mode 100644 docs/source/notebooks/handeye/results/handeye.pdf delete mode 100644 docs/source/notebooks/handeye/results/handeye_ap_2018-09-20 15:51:58.908574.pdf delete mode 100644 docs/source/notebooks/handeye/results/handeye_no_ap.pdf delete mode 100644 docs/source/notebooks/handeye/results/handeye_no_ap_2018-09-20 15:51:58.908574.pdf diff --git a/docs/source/notebooks/handeye/ACS2 in HandEye with Action Planning.ipynb b/docs/source/notebooks/handeye/ACS2 in HandEye with Action Planning.ipynb new file mode 100644 index 00000000..7e6d45be --- /dev/null +++ b/docs/source/notebooks/handeye/ACS2 in HandEye with Action Planning.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# ACS2 in HandEye with Action Planning\n", + "In this notebook, I will show the results of using Action Planning in HandEye environment. You can read more about the use of the HandEye environment in [ACS2 in HandEye](ACS2 in HandEye.ipynb). Here I will focus only on the results.\n", + "\n", + "I decided to not only see the results of just one experiment, but to see mean results of 100 experiments. It can be easily done, but of course will take a lot of time. For me, it took about 4 hours to run 100 experiments of HandEye3-v0 environment, 50 trials each. It will take of course more time for bigger environemnts (like HandEye5-v0 etc.), for more trials and for more experiments. The results are not, however, connected to time. I plotted all the results in a function of the number of trials. Time is irrelevant in this case. \n", + "\n", + "The results are easily read when they are in the form of a graph. Therefore, I used some functions to plot the numbers I get from running the experiments. They are of course mean values from all 100 experiments." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start with dependencies and constants." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "\n", + "import gym\n", + "import gym_handeye\n", + "\n", + "from lcs.agents.acs2 import ACS2, ClassifiersList, Configuration\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from examples.acs2.handeye.utils import calculate_performance\n", + "\n", + "TITLE_TEXT_SIZE=24\n", + "AXIS_TEXT_SIZE=18\n", + "LEGEND_TEXT_SIZE=16" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then I used some functions that are the same or similar to the ones from other Notebook - [ACS2 in HandEye](ACS2 in HandEye.ipynb). They might differ only because the ones here have an \"additional_info\" parameter." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def parse_metrics_to_df(explore_metrics, exploit_metrics):\n", + " def extract_details(row):\n", + " row['trial'] = row['agent']['trial']\n", + " row['steps'] = row['agent']['steps']\n", + " row['numerosity'] = row['agent']['numerosity']\n", + " row['reliable'] = row['agent']['reliable']\n", + " row['knowledge'] = row['performance']['knowledge']\n", + " row['with_block'] = row['performance']['with_block']\n", + " row['no_block'] = row['performance']['no_block']\n", + " return row\n", + "\n", + " # Load both metrics into data frame\n", + " explore_df = pd.DataFrame(explore_metrics)\n", + " exploit_df = pd.DataFrame(exploit_metrics)\n", + "\n", + " # Mark them with specific phase\n", + " explore_df['phase'] = 'explore'\n", + " exploit_df['phase'] = 'exploit'\n", + "\n", + " # Extract details\n", + " explore_df = explore_df.apply(extract_details, axis=1)\n", + " exploit_df = exploit_df.apply(extract_details, axis=1)\n", + "\n", + " # Adjuts exploit trial counter\n", + " exploit_df['trial'] = exploit_df.apply(\n", + " lambda r: r['trial'] + len(explore_df), axis=1)\n", + "\n", + " # Concatenate both dataframes\n", + " df = pd.concat([explore_df, exploit_df])\n", + " df.drop(['agent', 'environment', 'performance'], axis=1, inplace=True)\n", + " df.set_index('trial', inplace=True)\n", + "\n", + " return df\n", + "\n", + "\n", + "def plot_knowledge(df, ax=None, additional_info=\"\"):\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " explore_df = df.query(\"phase == 'explore'\")\n", + " exploit_df = df.query(\"phase == 'exploit'\")\n", + "\n", + " explore_df['knowledge'].plot(ax=ax, c='blue')\n", + " explore_df['with_block'].plot(ax=ax, c='green')\n", + " explore_df['no_block'].plot(ax=ax, c='yellow')\n", + " exploit_df['knowledge'].plot(ax=ax, c='red')\n", + " ax.axvline(x=len(explore_df), c='black', linestyle='dashed')\n", + "\n", + " ax.set_title(\"Achieved knowledge{}\".format(additional_info), fontsize=TITLE_TEXT_SIZE)\n", + " ax.set_xlabel(\"Trial\", fontsize=AXIS_TEXT_SIZE)\n", + " ax.set_ylabel(\"Knowledge [%]\", fontsize=AXIS_TEXT_SIZE)\n", + " ax.set_ylim([0, 105])\n", + " ax.legend(fontsize=LEGEND_TEXT_SIZE)\n", + "\n", + "\n", + "def plot_classifiers(df, ax=None):\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " explore_df = df.query(\"phase == 'explore'\")\n", + " exploit_df = df.query(\"phase == 'exploit'\")\n", + "\n", + " df['numerosity'].plot(ax=ax, c='blue')\n", + " df['reliable'].plot(ax=ax, c='red')\n", + "\n", + " ax.axvline(x=len(explore_df), c='black', linestyle='dashed')\n", + "\n", + " ax.set_title(\"Classifiers\", fontsize=TITLE_TEXT_SIZE)\n", + " ax.set_xlabel(\"Trial\", fontsize=AXIS_TEXT_SIZE)\n", + " ax.set_ylabel(\"Classifiers\", fontsize=AXIS_TEXT_SIZE)\n", + " ax.legend(fontsize=LEGEND_TEXT_SIZE)\n", + "\n", + "\n", + "def plot_performance(metrics_df, env_name, additional_info,\n", + " with_AP=\"\"):\n", + " plt.figure(figsize=(13, 10), dpi=100)\n", + " plt.suptitle(f'ACS2 Performance in {env_name} environment '\n", + " f'{additional_info}', fontsize=32)\n", + "\n", + " ax2 = plt.subplot(211)\n", + " plot_knowledge(metrics_df, ax2, with_AP)\n", + "\n", + " ax3 = plt.subplot(212)\n", + " plot_classifiers(metrics_df, ax3)\n", + "\n", + " plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, I had to write some functions to count the mean of all the values. I decided not to save all the results from all 100 experiments and then at the end count the mean, but to count it after each experiment.\n", + "\n", + "It is counted basically as follows:\n", + "\n", + " mean = (a + b)/2\n", + " mean = (mean * 2 + c)/3\n", + " mean = (mean * 3 + d)/4\n", + " \n", + "And so on." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def mean(i, row_mean, row, first, second):\n", + " return (row_mean[first][second]\n", + " * i + row[first][second]) / (i + 1)\n", + "\n", + "\n", + "def count_mean_values(i: int, metrics, mean_metrics):\n", + " new_metrics = metrics.copy()\n", + " for row, row_new, row_mean in zip(metrics, new_metrics, mean_metrics):\n", + " row_new['performance']['knowledge'] = mean(i, row_mean, row,\n", + " 'performance', 'knowledge')\n", + " row_new['performance']['with_block'] = mean(i, row_mean, row,\n", + " 'performance',\n", + " 'with_block')\n", + " row_new['performance']['no_block'] = mean(i, row_mean, row,\n", + " 'performance', 'no_block')\n", + " row_new['agent']['numerosity'] = mean(i, row_mean, row,\n", + " 'agent', 'numerosity')\n", + " row_new['agent']['steps'] = mean(i, row_mean, row,\n", + " 'agent', 'steps')\n", + " row_new['agent']['reliable'] = mean(i, row_mean, row,\n", + " 'agent', 'reliable')\n", + " return new_metrics\n", + "\n", + "\n", + "def plot_handeye_mean(number_of_tests=50, env_name='HandEye3-v0',\n", + " filename='mean_results/handeye.pdf', do_action_planning=True,\n", + " number_of_trials_explore=50, number_of_trials_exploit=2):\n", + " hand_eye = gym.make(env_name)\n", + " cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n,\n", + " epsilon=1.0,\n", + " do_ga=False,\n", + " do_action_planning=do_action_planning,\n", + " performance_fcn=calculate_performance)\n", + "\n", + " mean_metrics_he_exploit = []\n", + " mean_metrics_he_explore = []\n", + "\n", + " for i in range(number_of_tests):\n", + " # explore\n", + " agent_he = ACS2(cfg)\n", + " population_he_explore, metrics_he_explore = agent_he.explore(\n", + " hand_eye, number_of_trials_explore)\n", + "\n", + " # exploit\n", + " agent_he = ACS2(cfg, population_he_explore)\n", + " _, metrics_he_exploit = agent_he.exploit(hand_eye,\n", + " number_of_trials_exploit)\n", + "\n", + " mean_metrics_he_explore = count_mean_values(i, metrics_he_explore,\n", + " mean_metrics_he_explore)\n", + " mean_metrics_he_exploit = count_mean_values(i, metrics_he_exploit,\n", + " mean_metrics_he_exploit)\n", + "\n", + " he_metrics_df = parse_metrics_to_df(mean_metrics_he_explore,\n", + " mean_metrics_he_exploit)\n", + " if do_action_planning:\n", + " with_ap = \", with Action Planning\"\n", + " else:\n", + " with_ap = \", without Action Planning\"\n", + "\n", + " plot_performance(he_metrics_df, env_name,\n", + " '\\nmean for {} experiments'.format(number_of_tests),\n", + " with_ap)\n", + " plt.savefig(filename.replace(\" \", \"_\"), format='pdf', dpi=100)\n", + " return he_metrics_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, I decided to put both results (with Action Planning and without Action Planning) in one pdf document, just so it'll be easily compared. Running all 100 experiments once again would be a waste of time (a lot of time, since it takes 4 hours), so I just use the already counted mean results." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_both_performances(metrics_ap, metrics_no_ap, env_name,\n", + " additional_info):\n", + " plt.figure(figsize=(13, 10), dpi=100)\n", + " plt.suptitle(f'ACS2 Performance in {env_name} environment '\n", + " f'{additional_info}', fontsize=32)\n", + "\n", + " ax2 = plt.subplot(211)\n", + " plot_knowledge(metrics_ap, ax2, \", with Action Planning\")\n", + "\n", + " ax3 = plt.subplot(212)\n", + " plot_knowledge(metrics_no_ap, ax3, \", without Action Planning\")\n", + "\n", + " plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3)\n", + " \n", + "def plot_with_without_ap(filename, metrics_ap, metrics_no_ap):\n", + " plot_both_performances(metrics_ap, metrics_no_ap, env_name,\n", + " '\\nmean for {} experiments'.format(number_of_tests))\n", + " plt.savefig(filename.replace(\" \", \"_\"), format='pdf', dpi=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, I was ready to run the experiments. Since it took 4 hours, I do not recommend running it when you don't have enough time. You can do a test with 2 experiments (less than 10 minutes), just as I did below. \n", + "\n", + "I print a timestamp and elapsed time just to know how much time it took, but it is not an important part of the experiment." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time start: 2018-10-03 11:05:45.593276\n", + "done with AP, time: 2018-10-03 11:10:17.738208, elapsed: 0:04:32.144932\n", + "done without AP, time: 2018-10-03 11:13:01.205566, elapsed: 0:02:43.467358\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "env_name = 'HandEye3-v0'\n", + "number_of_tests = 2\n", + "number_of_trials_explore = 50\n", + "number_of_trials_exploit = 10\n", + "\n", + "start = datetime.datetime.now()\n", + "print(\"time start: {}\".format(start))\n", + "\n", + "metrics_ap = plot_handeye_mean(number_of_tests, env_name,\n", + " 'mean_results/{}_ap_{}_{}.pdf'.\n", + " format(env_name, number_of_tests, start),\n", + " do_action_planning=True,\n", + " number_of_trials_explore=\n", + " number_of_trials_explore,\n", + " number_of_trials_exploit=\n", + " number_of_trials_exploit)\n", + "\n", + "middle = datetime.datetime.now()\n", + "print(\"done with AP, time: {}, elapsed: {}\".format(middle, middle-start))\n", + "\n", + "metrics_no_ap = plot_handeye_mean(number_of_tests, env_name,\n", + " 'mean_results/{}_no_ap_{}_{}.pdf'.\n", + " format(env_name, number_of_tests, start),\n", + " do_action_planning=False,\n", + " number_of_trials_explore=\n", + " number_of_trials_explore,\n", + " number_of_trials_exploit=\n", + " number_of_trials_exploit)\n", + "\n", + "end = datetime.datetime.now()\n", + "print(\"done without AP, time: {}, elapsed: {}\".format(end, end-middle))\n", + "\n", + "plot_with_without_ap('mean_results/{}_both_{}_{}.pdf'.format(env_name,\n", + " number_of_tests,\n", + " start),\n", + " metrics_ap, metrics_no_ap)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 100 experiments results\n", + "For 100 experiment, the results are as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import IFrame\n", + "IFrame(\"mean_results/HandEye3-v0_both_100_2018-10-02_23:02:51.492298.pdf\", width=800, height=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that Action Planning makes gaining the knowledge, especially knowledge about transitions with block, a bit faster. It is as expected, although in the original article (\"Action-Planning in Anticipatory Classifier System\", Butz, Stolzmann) the results seem a little bit better (meaning faster when using Action Planning in comparison to not using it).\n", + "\n", + "Overall, Action Planning makes gaining the knowledge in HandEye3 environment faster than when not using it. I think that the results for bigger environments could be even better, but it's yet to check." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/notebooks/handeye/ACS2_in_HandEye.ipynb b/docs/source/notebooks/handeye/ACS2 in HandEye.ipynb similarity index 100% rename from docs/source/notebooks/handeye/ACS2_in_HandEye.ipynb rename to docs/source/notebooks/handeye/ACS2 in HandEye.ipynb diff --git a/docs/source/notebooks/handeye/results/HandEye2_ap_2018-09-20 16:01:56.258596.pdf 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z2>%g&>%{LE%M?KFp#2z|{v+(m`rtjb%zpu^7g(ucW0U_Dxj)gE40zugKo@IqfbBPS zIRvZ@z|Q@YG8l&4Vgq?<;Qfo!KuQ}7$ap_b4-DU5Kmkht2E@IeX9k92%_~sO91Mv5 zAoYilf@7f;_U%F5fuRDfF#(w%z%Idmb!-W+{h%OVRk2GyP~~9#-M_K|JXvGy?tcEh zAqC9IeiQq)72v@B8yjGf9i(i5HRT{>hqcoCZymsZ)b>-3z@$1zT?Nz&n*u?cz{p?2 zbOr;f@P0Y0B}e`m=zn(pS6>(~@Y!p>lpC}~@ z_ILllsIV{r^{ZbGVDcQa^M?^f{p#KmjQZ8F7oa@{ja~zg#6fBwcVLO7 zfthvC!2hfU_WAdF8Q8(^CddGJzYYC=zx)8PFaz%&s6b@kzl#B&;K0KE?*}6OD2Di5 z3@QZRqN568Ur0&)VT-YY`bEwENW_lspG+)=|H;I%?)PBioIL<@4N$0NXNg5@`#&I| zi^U%YlpOpB#{YUjVWsq6W#E?>-0hs53QC z7P6_puc~WgJGA5LwUdh0BQfuLjY#w?>sp4?>?YND1dMe zmxG}Y*tv!M``_45uz&Lav;kWcnb4h6-oS%=F3d;7on2So`3km&DnqW{PeDhxvdGx>0P!ayhgktYKEoA*#r?1{}^bwv^X@<3D=*nRw6P89VI572M`di+%mK#TwMi#}p( zXpw*Ai$?#$KVT<<{73Jh$bWSlD)Nu+!T^>0dmJzr;$J<531f{U_V0gs3Ws8i-(Pvi zf9ZpxpnuN~7{Hdlc?Fzl|1;kJ829fyM_^&xU;QG0CG4L(G}gErrVp4O|K=gECjM|a z;Ky PAW$d}7ng#TBIy4C(g41c From 8510f92a0add2d98e86b13e98af0cc8db56f1f45 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 3 Oct 2018 12:58:50 +0200 Subject: [PATCH 70/83] changes due to PEP8 --- .../notebooks/handeye/mean_results_handeye.py | 54 +++++++++---------- docs/source/notebooks/handeye/plot_handeye.py | 11 ++-- lcs/agents/acs2/ACS2.py | 14 ++--- .../action_planning/goal_sequence_searcher.py | 9 ++-- 4 files changed, 42 insertions(+), 46 deletions(-) diff --git a/docs/source/notebooks/handeye/mean_results_handeye.py b/docs/source/notebooks/handeye/mean_results_handeye.py index 10bb3772..f56d17c9 100644 --- a/docs/source/notebooks/handeye/mean_results_handeye.py +++ b/docs/source/notebooks/handeye/mean_results_handeye.py @@ -12,9 +12,9 @@ from examples.acs2.handeye.utils import calculate_performance -TITLE_TEXT_SIZE=24 -AXIS_TEXT_SIZE=18 -LEGEND_TEXT_SIZE=16 +TITLE_TEXT_SIZE = 24 +AXIS_TEXT_SIZE = 18 +LEGEND_TEXT_SIZE = 16 def parse_metrics_to_df(explore_metrics, exploit_metrics): @@ -65,7 +65,8 @@ def plot_knowledge(df, ax=None, additional_info=""): exploit_df['knowledge'].plot(ax=ax, c='red') ax.axvline(x=len(explore_df), c='black', linestyle='dashed') - ax.set_title("Achieved knowledge{}".format(additional_info), fontsize=TITLE_TEXT_SIZE) + ax.set_title("Achieved knowledge{}".format(additional_info), + fontsize=TITLE_TEXT_SIZE) ax.set_xlabel("Trial", fontsize=AXIS_TEXT_SIZE) ax.set_ylabel("Knowledge [%]", fontsize=AXIS_TEXT_SIZE) ax.set_ylim([0, 105]) @@ -121,8 +122,7 @@ def plot_both_performances(metrics_ap, metrics_no_ap, env_name, def mean(i, row_mean, row, first, second): - return (row_mean[first][second] - * i + row[first][second]) / (i + 1) + return (row_mean[first][second] * i + row[first][second]) / (i + 1) def count_mean_values(i: int, metrics, mean_metrics): @@ -145,8 +145,10 @@ def count_mean_values(i: int, metrics, mean_metrics): def plot_handeye_mean(number_of_tests=50, env_name='HandEye3-v0', - filename='mean_results/handeye.pdf', do_action_planning=True, - number_of_trials_explore=50, number_of_trials_exploit=2): + filename='mean_results/handeye.pdf', + do_action_planning=True, + number_of_trials_explore=50, + number_of_trials_exploit=2): hand_eye = gym.make(env_name) cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, epsilon=1.0, @@ -202,31 +204,23 @@ def plot_with_without_ap(filename, metrics_ap, metrics_no_ap): start = datetime.datetime.now() print("time start: {}".format(start)) - metrics_ap = plot_handeye_mean(number_of_tests, env_name, - 'mean_results/{}_ap_{}_{}.pdf'. - format(env_name, number_of_tests, start), - do_action_planning=True, - number_of_trials_explore= - number_of_trials_explore, - number_of_trials_exploit= - number_of_trials_exploit) + metrics_ap = plot_handeye_mean( + number_of_tests, env_name, 'mean_results/{}_ap_{}_{}.pdf' + .format(env_name, number_of_tests, start), do_action_planning=True, + number_of_trials_explore=number_of_trials_explore, + number_of_trials_exploit=number_of_trials_exploit) middle = datetime.datetime.now() - print("done with AP, time: {}, elapsed: {}".format(middle, middle-start)) + print("done with AP, time: {}, elapsed: {}".format(middle, middle - start)) - metrics_no_ap = plot_handeye_mean(number_of_tests, env_name, - 'mean_results/{}_no_ap_{}_{}.pdf'. - format(env_name, number_of_tests, start), - do_action_planning=False, - number_of_trials_explore= - number_of_trials_explore, - number_of_trials_exploit= - number_of_trials_exploit) + metrics_no_ap = plot_handeye_mean( + number_of_tests, env_name, 'mean_results/{}_no_ap_{}_{}.pdf'. + format(env_name, number_of_tests, start), do_action_planning=False, + number_of_trials_explore=number_of_trials_explore, + number_of_trials_exploit=number_of_trials_exploit) end = datetime.datetime.now() - print("done without AP, time: {}, elapsed: {}".format(end, end-middle)) + print("done without AP, time: {}, elapsed: {}".format(end, end - middle)) - plot_with_without_ap('mean_results/{}_both_{}_{}.pdf'.format(env_name, - number_of_tests, - start), - metrics_ap, metrics_no_ap) + plot_with_without_ap('mean_results/{}_both_{}_{}.pdf'.format( + env_name, number_of_tests, start), metrics_ap, metrics_no_ap) diff --git a/docs/source/notebooks/handeye/plot_handeye.py b/docs/source/notebooks/handeye/plot_handeye.py index e6e05065..9d84e10f 100644 --- a/docs/source/notebooks/handeye/plot_handeye.py +++ b/docs/source/notebooks/handeye/plot_handeye.py @@ -12,9 +12,9 @@ from examples.acs2.handeye.utils import calculate_performance -TITLE_TEXT_SIZE=24 -AXIS_TEXT_SIZE=18 -LEGEND_TEXT_SIZE=16 +TITLE_TEXT_SIZE = 24 +AXIS_TEXT_SIZE = 18 +LEGEND_TEXT_SIZE = 16 def parse_metrics_to_df(explore_metrics, exploit_metrics): @@ -71,6 +71,7 @@ def plot_knowledge(df, ax=None): ax.set_ylim([0, 105]) ax.legend(fontsize=LEGEND_TEXT_SIZE) + def plot_classifiers(df, ax=None): if ax is None: ax = plt.gca() @@ -144,10 +145,10 @@ def plot_handeye(env_name='HandEye3-v0', filename='results/handeye.pdf', do_action_planning=True) middle = datetime.datetime.now() - print("done with AP, time: {}, elapsed: {}".format(middle, middle-start)) + print("done with AP, time: {}, elapsed: {}".format(middle, middle - start)) plot_handeye(env_name, 'results/{}_no_ap_{}.pdf'.format(env_name, start), do_action_planning=False) end = datetime.datetime.now() - print("done without AP, time: {}, elapsed: {}".format(end, end-middle)) + print("done without AP, time: {}, elapsed: {}".format(end, end - middle)) diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index c4d99842..3ae0fb3a 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -294,12 +294,12 @@ def _run_action_planning(self, env, if act == -1: break - match_set = self.population.form_match_set(situation= - Perception(state)) + match_set = self.population.form_match_set( + situation=Perception(state)) if action_set is not None and prev_state is not None: - action_set.apply_alp( + ClassifiersList.apply_alp( self.population, - None, + match_set, action_set, Perception(prev_state), action, @@ -307,17 +307,17 @@ def _run_action_planning(self, env, time + steps, self.cfg.theta_exp, self.cfg) - action_set.apply_reinforcement_learning( + ClassifiersList.apply_reinforcement_learning( action_set, reward, 0, self.cfg.beta, self.cfg.gamma) if self.cfg.do_ga: - action_set.apply_ga( + ClassifiersList.apply_ga( time + steps, self.population, - None, + match_set, action_set, Perception(state), self.cfg.theta_ga, diff --git a/lcs/strategies/action_planning/goal_sequence_searcher.py b/lcs/strategies/action_planning/goal_sequence_searcher.py index 2b1c5138..0803fe2f 100644 --- a/lcs/strategies/action_planning/goal_sequence_searcher.py +++ b/lcs/strategies/action_planning/goal_sequence_searcher.py @@ -177,7 +177,7 @@ def _form_new_classifiers(classifiers_lists: List[ClassifiersList], def _form_sequence_forwards(self, i: int, backward_sequence_idx: int, - match_set_el: Classifier) -> List[int]: + match_set_el: Classifier) -> list: """ Forms sequence when it was found forwards. :param i: @@ -195,13 +195,14 @@ def _form_sequence_forwards(self, i: int, sequence_size += 1 # construct sequence - act_seq = [-1] * sequence_size + act_seq: list = [-1] * sequence_size j = 0 if i > 0: for j, cl in enumerate(self.forward_classifiers[i - 1]): act_seq[len(self.forward_classifiers[i - 1]) - j - 1] \ = cl.action j += 1 + act_seq[j] = match_set_el.action j += 1 if backward_sequence_idx > 0: @@ -212,7 +213,7 @@ def _form_sequence_forwards(self, i: int, def _form_sequence_backwards(self, i: int, forward_sequence_idx: int, - match_set_el: Classifier) -> List[int]: + match_set_el: Classifier) -> list: """ Forms sequence when it was found backwards. :param i: int @@ -230,7 +231,7 @@ def _form_sequence_backwards(self, i: int, sequence_size += 1 # construct sequence - act_seq = [-1] * sequence_size + act_seq: list = [-1] * sequence_size j = 0 if forward_sequence_idx > 0: for j, cl in enumerate(self.forward_classifiers[ From df83e280cef4fd9534da5df719f79ffeaa21be73 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 3 Oct 2018 13:17:08 +0200 Subject: [PATCH 71/83] changes due to PEP8 etc --- lcs/agents/acs2/ClassifiersList.py | 1 - tests/lcs/agents/acs2/test_Classifier.py | 27 ------------------------ 2 files changed, 28 deletions(-) diff --git a/lcs/agents/acs2/ClassifiersList.py b/lcs/agents/acs2/ClassifiersList.py index c4758a35..c502b6f5 100644 --- a/lcs/agents/acs2/ClassifiersList.py +++ b/lcs/agents/acs2/ClassifiersList.py @@ -9,7 +9,6 @@ import lcs.strategies.genetic_algorithms as ga import lcs.strategies.reinforcement_learning as rl from lcs import Perception, TypedList - from lcs.agents.acs2 import Configuration from . import Classifier diff --git a/tests/lcs/agents/acs2/test_Classifier.py b/tests/lcs/agents/acs2/test_Classifier.py index b2d83d93..fd1dd623 100644 --- a/tests/lcs/agents/acs2/test_Classifier.py +++ b/tests/lcs/agents/acs2/test_Classifier.py @@ -758,30 +758,3 @@ def test_get_best_anticipation(self, cfg, _p0, _result): # then assert result0 == _result - - @pytest.mark.parametrize("_c, _p, _result", [ - ('########', '10011001', True), - ('1#######', '10011001', True), - ('0#######', '10011001', False), - ]) - def test_should_match_perception(self, _c, _p, _result): - # given - c = Condition(_c) - p = Perception(_p) - - # then - assert c.does_match(p) is _result - - @pytest.mark.parametrize("_c, _other, _result", [ - ('########', '10011001', True), - ('1#######', '10011001', True), - ('0#######', '10011001', False), - ('####0###', '#1O##O##', True), - ]) - def test_should_match_condition(self, _c, _other, _result): - # given - c = Condition(_c) - other = Condition(_other) - - # then - assert c.does_match(other) is _result From 3f076a5dedba4b69dbe9b925d241db965004c93a Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 3 Oct 2018 13:24:09 +0200 Subject: [PATCH 72/83] minor changes --- lcs/agents/acs2/ACS2.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index 3ae0fb3a..6ed31c88 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -17,7 +17,7 @@ class ACS2(Agent): def __init__(self, cfg: Configuration, - population: ClassifiersList = None) -> None: + population: ClassifiersList=None) -> None: self.cfg = cfg if population: @@ -51,7 +51,6 @@ def explore_exploit(self, env, trials): :param trials: number of trials :return: population of classifiers and metrics """ - def switch_phases(env, steps, current_trial): if current_trial % 2 == 0: return self._run_trial_explore(env, steps) From a0840772b27be04479b1682496b1eaa2efa43c9b Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 3 Oct 2018 14:37:15 +0200 Subject: [PATCH 73/83] tests for search one for/backward step --- .../test_goal_sequence_searcher.py | 174 ++++++++++++++++-- 1 file changed, 157 insertions(+), 17 deletions(-) diff --git a/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py b/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py index baf8ed1d..6e0775b9 100644 --- a/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py +++ b/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py @@ -30,23 +30,6 @@ def test_does_contain_state(self): assert result_1 == 1 assert result_none is None - def test_search_goal_sequence_1(self): - # given - gs = GoalSequenceSearcher() - start = Perception("11111111") - goal = Perception("11111110") - - empty_list = ClassifiersList() - - # when - result = gs.search_goal_sequence(empty_list, start=start, goal=goal) - - assert result == [] - - @pytest.mark.skip(reason="not implemented yet") - def test_search_goal_sequence(self): - pass - def test_form_new_classifiers_1(self, cfg): # given gs = GoalSequenceSearcher() @@ -285,3 +268,160 @@ def test_form_sequence_backwards_5(self, cfg): assert cl1.action in seq assert cl2.action in seq assert seq == [cl2.action, cl0.action, cl1.action] + + def test_search_goal_sequence_1(self): + # given + gs = GoalSequenceSearcher() + start = Perception("11111111") + goal = Perception("11111110") + + empty_list = ClassifiersList() + + # when + result = gs.search_goal_sequence(empty_list, start=start, goal=goal) + + assert result == [] + + @pytest.mark.skip(reason="not implemented yet") + def test_search_goal_sequence_2(self): + pass + + def test_search_one_forward_step_1(self, cfg): + # given + gs = GoalSequenceSearcher() + start = "01111111" + goal = "00111111" + gs.forward_perceptions.append(Perception(start)) + gs.backward_perceptions.append(Perception(goal)) + reliable_classifiers = ClassifiersList(Classifier(condition="#1######", + action=1, + effect="#0######", + cfg=cfg)) + forward_size = 1 + forward_point = 0 + + # when + act_seq, size = gs._search_one_forward_step(reliable_classifiers, + forward_size, + forward_point) + + # then + assert act_seq == [1] + + def test_search_one_forward_step_2(self, cfg): + # given + gs = GoalSequenceSearcher() + start = "01111111" + goal = "10111111" + gs.forward_perceptions.append(Perception(start)) + gs.backward_perceptions.append(Perception(goal)) + reliable_classifiers = ClassifiersList( + Classifier(condition="#1######", action=1, effect="#0######", + cfg=cfg) + ) + forward_size = 1 + forward_point = 0 + + # when + act_seq, size = gs._search_one_forward_step(reliable_classifiers, + forward_size, + forward_point) + + # then + assert act_seq is None + assert len(gs.forward_classifiers) == 1 + + def test_search_one_forward_step_3(self, cfg): + # given + gs = GoalSequenceSearcher() + start = "01111111" + goal = "10111111" + gs.forward_perceptions.append(Perception(start)) + gs.backward_perceptions.append(Perception(goal)) + reliable_classifiers = ClassifiersList( + Classifier(condition="#1######", action=1, effect="#0######", + cfg=cfg), + Classifier(condition="0#######", action=1, effect="1#######", + cfg=cfg) + ) + forward_size = 1 + forward_point = 0 + + # when + act_seq, size = gs._search_one_forward_step(reliable_classifiers, + forward_size, + forward_point) + + # then + assert act_seq is None + assert len(gs.forward_classifiers) == 2 + + def test_search_one_backward_step_1(self, cfg): + # given + gs = GoalSequenceSearcher() + start = "01111111" + goal = "00111111" + gs.forward_perceptions.append(Perception(start)) + gs.backward_perceptions.append(Perception(goal)) + reliable_classifiers = ClassifiersList(Classifier(condition="#1######", + action=1, + effect="#0######", + cfg=cfg)) + forward_size = 1 + forward_point = 0 + + # when + act_seq, size = gs._search_one_backward_step(reliable_classifiers, + forward_size, + forward_point) + + # then + assert act_seq == [1] + + def test_search_one_backward_step_2(self, cfg): + # given + gs = GoalSequenceSearcher() + start = "01111111" + goal = "10111111" + gs.forward_perceptions.append(Perception(start)) + gs.backward_perceptions.append(Perception(goal)) + reliable_classifiers = ClassifiersList( + Classifier(condition="#1######", action=1, effect="#0######", + cfg=cfg) + ) + forward_size = 1 + forward_point = 0 + + # when + act_seq, size = gs._search_one_backward_step(reliable_classifiers, + forward_size, + forward_point) + + # then + assert act_seq is None + assert len(gs.backward_classifiers) == 1 + + def test_search_one_backward_step_3(self, cfg): + # given + gs = GoalSequenceSearcher() + start = "01111111" + goal = "10111111" + gs.forward_perceptions.append(Perception(start)) + gs.backward_perceptions.append(Perception(goal)) + reliable_classifiers = ClassifiersList( + Classifier(condition="#1######", action=1, effect="#0######", + cfg=cfg), + Classifier(condition="0#######", action=1, effect="1#######", + cfg=cfg) + ) + forward_size = 1 + forward_point = 0 + + # when + act_seq, size = gs._search_one_backward_step(reliable_classifiers, + forward_size, + forward_point) + + # then + assert act_seq is None + assert len(gs.backward_classifiers) == 2 From 7a307efd5942346ba0ba8246c42842427ec180a8 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 3 Oct 2018 15:09:33 +0200 Subject: [PATCH 74/83] test for search goal sequence --- .../action_planning/test_action_planning.py | 60 ++++++++++++- .../test_goal_sequence_searcher.py | 88 +++++++++++++++---- 2 files changed, 129 insertions(+), 19 deletions(-) diff --git a/tests/lcs/strategies/action_planning/test_action_planning.py b/tests/lcs/strategies/action_planning/test_action_planning.py index e58377f4..7107692a 100644 --- a/tests/lcs/strategies/action_planning/test_action_planning.py +++ b/tests/lcs/strategies/action_planning/test_action_planning.py @@ -5,14 +5,14 @@ from lcs import Perception from lcs.agents.acs2 import Configuration, ClassifiersList, Classifier from lcs.strategies.action_planning.action_planning import \ - get_quality_classifiers_list, exists_classifier + get_quality_classifiers_list, exists_classifier, search_goal_sequence class TestActionPlanning: @pytest.fixture def cfg(self): - return Configuration(8, 8) + return Configuration(8, 8, theta_r=0.9) def test_get_quality_classifiers_list(self, cfg): # given @@ -89,3 +89,59 @@ def test_exists_classifier(self, cfg): # then assert result0 is False assert result1 is True + + def test_search_goal_sequence_1(self, cfg): + # given + start = "01111111" + goal = "00111111" + + classifiers = ClassifiersList( + Classifier(condition="#1######", action=1, effect="#0######", + quality=0.88, cfg=cfg), + Classifier(condition="#1######", action=1, effect="#0######", + quality=0.92, cfg=cfg) + ) + + # when + result = search_goal_sequence(classifiers, start, goal, cfg) + + # then + assert result == [1] + + def test_search_goal_sequence_2(self, cfg): + # given + start = "01111111" + goal = "00111111" + + classifiers = ClassifiersList( + Classifier(condition="#1######", action=1, effect="#0######", + quality=0.88, cfg=cfg), + Classifier(condition="#0######", action=1, effect="#1######", + quality=0.98, cfg=cfg) + ) + + # when + result = search_goal_sequence(classifiers, start, goal, cfg) + + # then + assert result == [] + + def test_search_goal_sequence_3(self, cfg): + # given + start = "01111111" + goal = "10111111" + + classifiers = ClassifiersList( + Classifier(condition="#1######", action=1, effect="#0######", + quality=0.94, cfg=cfg), + Classifier(condition="0#######", action=2, effect="1#######", + quality=0.98, cfg=cfg), + ) + + # when + result = search_goal_sequence(classifiers, start, goal, cfg) + + # then + assert len(result) == 2 + assert 1 in result + assert 2 in result diff --git a/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py b/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py index 6e0775b9..794d7ac9 100644 --- a/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py +++ b/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py @@ -269,23 +269,6 @@ def test_form_sequence_backwards_5(self, cfg): assert cl2.action in seq assert seq == [cl2.action, cl0.action, cl1.action] - def test_search_goal_sequence_1(self): - # given - gs = GoalSequenceSearcher() - start = Perception("11111111") - goal = Perception("11111110") - - empty_list = ClassifiersList() - - # when - result = gs.search_goal_sequence(empty_list, start=start, goal=goal) - - assert result == [] - - @pytest.mark.skip(reason="not implemented yet") - def test_search_goal_sequence_2(self): - pass - def test_search_one_forward_step_1(self, cfg): # given gs = GoalSequenceSearcher() @@ -425,3 +408,74 @@ def test_search_one_backward_step_3(self, cfg): # then assert act_seq is None assert len(gs.backward_classifiers) == 2 + + def test_search_goal_sequence_1(self): + # given + gs = GoalSequenceSearcher() + start = Perception("11111111") + goal = Perception("11111110") + + empty_list = ClassifiersList() + + # when + result = gs.search_goal_sequence(empty_list, start=start, goal=goal) + + # then + assert result == [] + + def test_search_goal_sequence_2(self, cfg): + # given + gs = GoalSequenceSearcher() + start = "01111111" + goal = "10111111" + + reliable_classifiers = ClassifiersList( + Classifier(condition="#1######", action=1, effect="#0######", + cfg=cfg), + Classifier(condition="0#######", action=2, effect="1#######", + cfg=cfg) + ) + + # when + result = gs.search_goal_sequence(reliable_classifiers, start=start, + goal=goal) + # then + assert 1 in result + assert 2 in result + assert len(result) == 2 + + def test_search_goal_sequence_3(self, cfg): + # given + gs = GoalSequenceSearcher() + start = "01111111" + goal = "10111111" + + reliable_classifiers = ClassifiersList( + Classifier(condition="#1######", action=1, effect="#0######", + cfg=cfg), + Classifier(condition="#0######", action=3, effect="#1######", + cfg=cfg), + ) + + # when + result = gs.search_goal_sequence(reliable_classifiers, start=start, + goal=goal) + # then + assert result == [] + + def test_search_goal_sequence_4(self, cfg): + # given + gs = GoalSequenceSearcher() + start = "01111111" + goal = "00111111" + + reliable_classifiers = ClassifiersList( + Classifier(condition="#1######", action=1, effect="#0######", + cfg=cfg) + ) + + # when + result = gs.search_goal_sequence(reliable_classifiers, start=start, + goal=goal) + # then + assert result == [1] From 0a6ffea61c70e3fa36b4ec6529bc310122e64932 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 3 Oct 2018 15:56:37 +0200 Subject: [PATCH 75/83] added tests for HandEye --- tests/integration/acs2/test_HandEye.py | 58 ++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 tests/integration/acs2/test_HandEye.py diff --git a/tests/integration/acs2/test_HandEye.py b/tests/integration/acs2/test_HandEye.py new file mode 100644 index 00000000..bff4dc87 --- /dev/null +++ b/tests/integration/acs2/test_HandEye.py @@ -0,0 +1,58 @@ +import gym +import gym_handeye +import pytest + +from examples.acs2.handeye.utils import calculate_performance +from lcs.agents.acs2 import Configuration, ACS2 + + +class TestHandEye: + + @pytest.fixture + def env(self): + return gym.make('HandEye3-v0') + + def test_initialize_environment(self, env): + assert env is not None + + def test_should_gain_knowledge(self, env): + # given + cfg = Configuration(env.observation_space.n, + env.action_space.n, + epsilon=1.0, + do_ga=False, + do_action_planning=True, + action_planning_frequency=50, + performance_fcn=calculate_performance) + agent = ACS2(cfg) + + # when + population, metrics = agent.explore(env, 10) + + # then + assert metrics[-1]['performance']['knowledge'] > 0.0 + assert metrics[-1]['performance']['with_block'] > 0.0 + assert metrics[-1]['performance']['no_block'] > 0.0 + + def test_should_evaluate_knowledge(self, env): + # given + cfg = Configuration(env.observation_space.n, + env.action_space.n, + epsilon=1.0, + do_ga=False, + do_action_planning=True, + action_planning_frequency=50, + performance_fcn=calculate_performance) + agent = ACS2(cfg) + + # when + population, metrics = agent.explore(env, 10) + + # then + for metric in metrics: + assert metric['performance']['knowledge'] >= 0.0 + assert metric['performance']['with_block'] >= 0.0 + assert metric['performance']['no_block'] >= 0.0 + assert metric['performance']['knowledge'] <= 100.0 + assert metric['performance']['with_block'] <= 100.0 + assert metric['performance']['no_block'] <= 100.0 From 8b154760e4856c787cad1f8995c53c840eb53750 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Thu, 4 Oct 2018 21:54:47 +0200 Subject: [PATCH 76/83] experiments of HandEye4 --- ...Eye4-v0_ap_50_2018-10-03_22:55:48.735472.pdf | Bin 0 -> 21707 bytes ...e4-v0_both_50_2018-10-03_22:55:48.735472.pdf | Bin 0 -> 22216 bytes ...4-v0_no_ap_50_2018-10-03_22:55:48.735472.pdf | Bin 0 -> 21694 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/source/notebooks/handeye/mean_results/HandEye4-v0_ap_50_2018-10-03_22:55:48.735472.pdf create mode 100644 docs/source/notebooks/handeye/mean_results/HandEye4-v0_both_50_2018-10-03_22:55:48.735472.pdf create mode 100644 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changes --- .../source/notebooks/handeye/mean_results_handeye.py | 11 +++++++++++ lcs/agents/acs2/ACS2.py | 3 ++- lcs/strategies/action_planning/action_planning.py | 8 ++++---- .../action_planning/test_action_planning.py | 8 ++++---- .../action_planning/test_goal_sequence_searcher.py | 12 ++++++------ 5 files changed, 27 insertions(+), 15 deletions(-) diff --git a/docs/source/notebooks/handeye/mean_results_handeye.py b/docs/source/notebooks/handeye/mean_results_handeye.py index f56d17c9..d0ee9834 100644 --- a/docs/source/notebooks/handeye/mean_results_handeye.py +++ b/docs/source/notebooks/handeye/mean_results_handeye.py @@ -160,6 +160,9 @@ def plot_handeye_mean(number_of_tests=50, env_name='HandEye3-v0', mean_metrics_he_explore = [] for i in range(number_of_tests): + # the below line can be un-commented for experiments + # print(i, datetime.datetime.now()) + # explore agent_he = ACS2(cfg) population_he_explore, metrics_he_explore = agent_he.explore( @@ -224,3 +227,11 @@ def plot_with_without_ap(filename, metrics_ap, metrics_no_ap): plot_with_without_ap('mean_results/{}_both_{}_{}.pdf'.format( env_name, number_of_tests, start), metrics_ap, metrics_no_ap) + + metrics_ap.to_csv('mean_results/{}_ap_{}_{}.csv'. + format(env_name, number_of_tests, start). + replace(' ', '_')) + + metrics_no_ap.to_csv('mean_results/{}_no_ap_{}_{}.txt'. + format(env_name, number_of_tests, start). + replace(' ', '_')) diff --git a/lcs/agents/acs2/ACS2.py b/lcs/agents/acs2/ACS2.py index 6ed31c88..2c9f1e3c 100644 --- a/lcs/agents/acs2/ACS2.py +++ b/lcs/agents/acs2/ACS2.py @@ -285,7 +285,8 @@ def _run_action_planning(self, env, break act_sequence = search_goal_sequence(self.population, state, - goal_situation, self.cfg) + goal_situation, + self.cfg.theta_r) # Execute the found sequence and learn during executing i = 0 diff --git a/lcs/strategies/action_planning/action_planning.py b/lcs/strategies/action_planning/action_planning.py index a87a0bfb..0c058f41 100644 --- a/lcs/strategies/action_planning/action_planning.py +++ b/lcs/strategies/action_planning/action_planning.py @@ -31,8 +31,7 @@ def exists_classifier(classifiers: ClassifiersList, def get_quality_classifiers_list(classifiers: ClassifiersList, - quality: float, - cfg: Configuration = None) -> ClassifiersList: + quality: float) -> ClassifiersList: """ Constructs classifier list out of a list with q > quality. :param classifiers: @@ -50,18 +49,19 @@ def get_quality_classifiers_list(classifiers: ClassifiersList, def search_goal_sequence(classifiers: ClassifiersList, start: str, goal: str, - cfg: Configuration) -> list: + theta_r: int) -> list: """ Searches a path from start to goal using a bidirectional method in the environmental model (i.e. the list of reliable classifiers). :param classifiers: :param start: Perception :param goal: Perception + :param theta_r: quality theta_r :return: Sequence of actions """ reliable_classifiers = \ get_quality_classifiers_list(classifiers, - quality=cfg.theta_r) + quality=theta_r) return GoalSequenceSearcher().search_goal_sequence( reliable_classifiers, start, goal) diff --git a/tests/lcs/strategies/action_planning/test_action_planning.py b/tests/lcs/strategies/action_planning/test_action_planning.py index 7107692a..a11dce66 100644 --- a/tests/lcs/strategies/action_planning/test_action_planning.py +++ b/tests/lcs/strategies/action_planning/test_action_planning.py @@ -36,7 +36,7 @@ def test_get_quality_classifiers_list(self, cfg): population.append(c4) # when - match_set = get_quality_classifiers_list(population, 0.5, cfg) + match_set = get_quality_classifiers_list(population, 0.5) # then assert 2 == len(match_set) @@ -103,7 +103,7 @@ def test_search_goal_sequence_1(self, cfg): ) # when - result = search_goal_sequence(classifiers, start, goal, cfg) + result = search_goal_sequence(classifiers, start, goal, cfg.theta_r) # then assert result == [1] @@ -121,7 +121,7 @@ def test_search_goal_sequence_2(self, cfg): ) # when - result = search_goal_sequence(classifiers, start, goal, cfg) + result = search_goal_sequence(classifiers, start, goal, cfg.theta_r) # then assert result == [] @@ -139,7 +139,7 @@ def test_search_goal_sequence_3(self, cfg): ) # when - result = search_goal_sequence(classifiers, start, goal, cfg) + result = search_goal_sequence(classifiers, start, goal, cfg.theta_r) # then assert len(result) == 2 diff --git a/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py b/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py index 794d7ac9..5677024c 100644 --- a/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py +++ b/tests/lcs/strategies/action_planning/test_goal_sequence_searcher.py @@ -355,8 +355,8 @@ def test_search_one_backward_step_1(self, cfg): # when act_seq, size = gs._search_one_backward_step(reliable_classifiers, - forward_size, - forward_point) + forward_size, + forward_point) # then assert act_seq == [1] @@ -377,8 +377,8 @@ def test_search_one_backward_step_2(self, cfg): # when act_seq, size = gs._search_one_backward_step(reliable_classifiers, - forward_size, - forward_point) + forward_size, + forward_point) # then assert act_seq is None @@ -402,8 +402,8 @@ def test_search_one_backward_step_3(self, cfg): # when act_seq, size = gs._search_one_backward_step(reliable_classifiers, - forward_size, - forward_point) + forward_size, + forward_point) # then assert act_seq is None From 8073e46d8768c852cc016682cf65eb00b776a5ba Mon Sep 17 00:00:00 2001 From: e-dzia Date: Sat, 6 Oct 2018 14:01:35 +0200 Subject: [PATCH 79/83] updated notebooks and deleted pdf result files --- ...ACS2 in HandEye with Action Planning.ipynb | 445 ---------------- .../notebooks/handeye/ACS2 in HandEye.ipynb | 481 ++++++++++++++++-- ...dEye2-v0_ap_2018-09-27_15:27:16.955187.pdf | Bin 17227 -> 0 bytes ...ye2-v0_both_2018-09-27_15:27:16.955187.pdf | Bin 16194 -> 0 bytes ...e2-v0_no_ap_2018-09-27_15:27:16.955187.pdf | Bin 17223 -> 0 bytes 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You can read more about the use of the HandEye environment in [ACS2 in HandEye](ACS2 in HandEye.ipynb). Here I will focus only on the results.\n", - "\n", - "I decided to not only see the results of just one experiment, but to see mean results of 100 experiments. It can be easily done, but of course will take a lot of time. For me, it took about 4 hours to run 100 experiments of HandEye3-v0 environment, 50 trials each. It will take of course more time for bigger environemnts (like HandEye5-v0 etc.), for more trials and for more experiments. The results are not, however, connected to time. I plotted all the results in a function of the number of trials. Time is irrelevant in this case. \n", - "\n", - "The results are easily read when they are in the form of a graph. Therefore, I used some functions to plot the numbers I get from running the experiments. They are of course mean values from all 100 experiments." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start with dependencies and constants." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "\n", - "import gym\n", - "import gym_handeye\n", - "\n", - "from lcs.agents.acs2 import ACS2, ClassifiersList, Configuration\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from examples.acs2.handeye.utils import calculate_performance\n", - "\n", - "TITLE_TEXT_SIZE=24\n", - "AXIS_TEXT_SIZE=18\n", - "LEGEND_TEXT_SIZE=16" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then I used some functions that are the same or similar to the ones from other Notebook - [ACS2 in HandEye](ACS2 in HandEye.ipynb). They might differ only because the ones here have an \"additional_info\" parameter." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def parse_metrics_to_df(explore_metrics, exploit_metrics):\n", - " def extract_details(row):\n", - " row['trial'] = row['agent']['trial']\n", - " row['steps'] = row['agent']['steps']\n", - " row['numerosity'] = row['agent']['numerosity']\n", - " row['reliable'] = row['agent']['reliable']\n", - " row['knowledge'] = row['performance']['knowledge']\n", - " row['with_block'] = row['performance']['with_block']\n", - " row['no_block'] = row['performance']['no_block']\n", - " return row\n", - "\n", - " # Load both metrics into data frame\n", - " explore_df = pd.DataFrame(explore_metrics)\n", - " exploit_df = pd.DataFrame(exploit_metrics)\n", - "\n", - " # Mark them with specific phase\n", - " explore_df['phase'] = 'explore'\n", - " exploit_df['phase'] = 'exploit'\n", - "\n", - " # Extract details\n", - " explore_df = explore_df.apply(extract_details, axis=1)\n", - " exploit_df = exploit_df.apply(extract_details, axis=1)\n", - "\n", - " # Adjuts exploit trial counter\n", - " exploit_df['trial'] = exploit_df.apply(\n", - " lambda r: r['trial'] + len(explore_df), axis=1)\n", - "\n", - " # Concatenate both dataframes\n", - " df = pd.concat([explore_df, exploit_df])\n", - " df.drop(['agent', 'environment', 'performance'], axis=1, inplace=True)\n", - " df.set_index('trial', inplace=True)\n", - "\n", - " return df\n", - "\n", - "\n", - "def plot_knowledge(df, ax=None, additional_info=\"\"):\n", - " if ax is None:\n", - " ax = plt.gca()\n", - "\n", - " explore_df = df.query(\"phase == 'explore'\")\n", - " exploit_df = df.query(\"phase == 'exploit'\")\n", - "\n", - " explore_df['knowledge'].plot(ax=ax, c='blue')\n", - " explore_df['with_block'].plot(ax=ax, c='green')\n", - " explore_df['no_block'].plot(ax=ax, c='yellow')\n", - " exploit_df['knowledge'].plot(ax=ax, c='red')\n", - " ax.axvline(x=len(explore_df), c='black', linestyle='dashed')\n", - "\n", - " ax.set_title(\"Achieved knowledge{}\".format(additional_info), fontsize=TITLE_TEXT_SIZE)\n", - " ax.set_xlabel(\"Trial\", fontsize=AXIS_TEXT_SIZE)\n", - " ax.set_ylabel(\"Knowledge [%]\", fontsize=AXIS_TEXT_SIZE)\n", - " ax.set_ylim([0, 105])\n", - " ax.legend(fontsize=LEGEND_TEXT_SIZE)\n", - "\n", - "\n", - "def plot_classifiers(df, ax=None):\n", - " if ax is None:\n", - " ax = plt.gca()\n", - "\n", - " explore_df = df.query(\"phase == 'explore'\")\n", - " exploit_df = df.query(\"phase == 'exploit'\")\n", - "\n", - " df['numerosity'].plot(ax=ax, c='blue')\n", - " df['reliable'].plot(ax=ax, c='red')\n", - "\n", - " ax.axvline(x=len(explore_df), c='black', linestyle='dashed')\n", - "\n", - " ax.set_title(\"Classifiers\", fontsize=TITLE_TEXT_SIZE)\n", - " ax.set_xlabel(\"Trial\", fontsize=AXIS_TEXT_SIZE)\n", - " ax.set_ylabel(\"Classifiers\", fontsize=AXIS_TEXT_SIZE)\n", - " ax.legend(fontsize=LEGEND_TEXT_SIZE)\n", - "\n", - "\n", - "def plot_performance(metrics_df, env_name, additional_info,\n", - " with_AP=\"\"):\n", - " plt.figure(figsize=(13, 10), dpi=100)\n", - " plt.suptitle(f'ACS2 Performance in {env_name} environment '\n", - " f'{additional_info}', fontsize=32)\n", - "\n", - " ax2 = plt.subplot(211)\n", - " plot_knowledge(metrics_df, ax2, with_AP)\n", - "\n", - " ax3 = plt.subplot(212)\n", - " plot_classifiers(metrics_df, ax3)\n", - "\n", - " plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, I had to write some functions to count the mean of all the values. I decided not to save all the results from all 100 experiments and then at the end count the mean, but to count it after each experiment.\n", - "\n", - "It is counted basically as follows:\n", - "\n", - " mean = (a + b)/2\n", - " mean = (mean * 2 + c)/3\n", - " mean = (mean * 3 + d)/4\n", - " \n", - "And so on." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def mean(i, row_mean, row, first, second):\n", - " return (row_mean[first][second]\n", - " * i + row[first][second]) / (i + 1)\n", - "\n", - "\n", - "def count_mean_values(i: int, metrics, mean_metrics):\n", - " new_metrics = metrics.copy()\n", - " for row, row_new, row_mean in zip(metrics, new_metrics, mean_metrics):\n", - " row_new['performance']['knowledge'] = mean(i, row_mean, row,\n", - " 'performance', 'knowledge')\n", - " row_new['performance']['with_block'] = mean(i, row_mean, row,\n", - " 'performance',\n", - " 'with_block')\n", - " row_new['performance']['no_block'] = mean(i, row_mean, row,\n", - " 'performance', 'no_block')\n", - " row_new['agent']['numerosity'] = mean(i, row_mean, row,\n", - " 'agent', 'numerosity')\n", - " row_new['agent']['steps'] = mean(i, row_mean, row,\n", - " 'agent', 'steps')\n", - " row_new['agent']['reliable'] = mean(i, row_mean, row,\n", - " 'agent', 'reliable')\n", - " return new_metrics\n", - "\n", - "\n", - "def plot_handeye_mean(number_of_tests=50, env_name='HandEye3-v0',\n", - " filename='mean_results/handeye.pdf', do_action_planning=True,\n", - " number_of_trials_explore=50, number_of_trials_exploit=2):\n", - " hand_eye = gym.make(env_name)\n", - " cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n,\n", - " epsilon=1.0,\n", - " do_ga=False,\n", - " do_action_planning=do_action_planning,\n", - " performance_fcn=calculate_performance)\n", - "\n", - " mean_metrics_he_exploit = []\n", - " mean_metrics_he_explore = []\n", - "\n", - " for i in range(number_of_tests):\n", - " # explore\n", - " agent_he = ACS2(cfg)\n", - " population_he_explore, metrics_he_explore = agent_he.explore(\n", - " hand_eye, number_of_trials_explore)\n", - "\n", - " # exploit\n", - " agent_he = ACS2(cfg, population_he_explore)\n", - " _, metrics_he_exploit = agent_he.exploit(hand_eye,\n", - " number_of_trials_exploit)\n", - "\n", - " mean_metrics_he_explore = count_mean_values(i, metrics_he_explore,\n", - " mean_metrics_he_explore)\n", - " mean_metrics_he_exploit = count_mean_values(i, metrics_he_exploit,\n", - " mean_metrics_he_exploit)\n", - "\n", - " he_metrics_df = parse_metrics_to_df(mean_metrics_he_explore,\n", - " mean_metrics_he_exploit)\n", - " if do_action_planning:\n", - " with_ap = \", with Action Planning\"\n", - " else:\n", - " with_ap = \", without Action Planning\"\n", - "\n", - " plot_performance(he_metrics_df, env_name,\n", - " '\\nmean for {} experiments'.format(number_of_tests),\n", - " with_ap)\n", - " plt.savefig(filename.replace(\" \", \"_\"), format='pdf', dpi=100)\n", - " return he_metrics_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, I decided to put both results (with Action Planning and without Action Planning) in one pdf document, just so it'll be easily compared. Running all 100 experiments once again would be a waste of time (a lot of time, since it takes 4 hours), so I just use the already counted mean results." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_both_performances(metrics_ap, metrics_no_ap, env_name,\n", - " additional_info):\n", - " plt.figure(figsize=(13, 10), dpi=100)\n", - " plt.suptitle(f'ACS2 Performance in {env_name} environment '\n", - " f'{additional_info}', fontsize=32)\n", - "\n", - " ax2 = plt.subplot(211)\n", - " plot_knowledge(metrics_ap, ax2, \", with Action Planning\")\n", - "\n", - " ax3 = plt.subplot(212)\n", - " plot_knowledge(metrics_no_ap, ax3, \", without Action Planning\")\n", - "\n", - " plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3)\n", - " \n", - "def plot_with_without_ap(filename, metrics_ap, metrics_no_ap):\n", - " plot_both_performances(metrics_ap, metrics_no_ap, env_name,\n", - " '\\nmean for {} experiments'.format(number_of_tests))\n", - " plt.savefig(filename.replace(\" \", \"_\"), format='pdf', dpi=100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, I was ready to run the experiments. Since it took 4 hours, I do not recommend running it when you don't have enough time. You can do a test with 2 experiments (less than 10 minutes), just as I did below. \n", - "\n", - "I print a timestamp and elapsed time just to know how much time it took, but it is not an important part of the experiment." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time start: 2018-10-03 11:05:45.593276\n", - "done with AP, time: 2018-10-03 11:10:17.738208, elapsed: 0:04:32.144932\n", - "done without AP, time: 2018-10-03 11:13:01.205566, elapsed: 0:02:43.467358\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "env_name = 'HandEye3-v0'\n", - "number_of_tests = 2\n", - "number_of_trials_explore = 50\n", - "number_of_trials_exploit = 10\n", - "\n", - "start = datetime.datetime.now()\n", - "print(\"time start: {}\".format(start))\n", - "\n", - "metrics_ap = plot_handeye_mean(number_of_tests, env_name,\n", - " 'mean_results/{}_ap_{}_{}.pdf'.\n", - " format(env_name, number_of_tests, start),\n", - " do_action_planning=True,\n", - " number_of_trials_explore=\n", - " number_of_trials_explore,\n", - " number_of_trials_exploit=\n", - " number_of_trials_exploit)\n", - "\n", - "middle = datetime.datetime.now()\n", - "print(\"done with AP, time: {}, elapsed: {}\".format(middle, middle-start))\n", - "\n", - "metrics_no_ap = plot_handeye_mean(number_of_tests, env_name,\n", - " 'mean_results/{}_no_ap_{}_{}.pdf'.\n", - " format(env_name, number_of_tests, start),\n", - " do_action_planning=False,\n", - " number_of_trials_explore=\n", - " number_of_trials_explore,\n", - " number_of_trials_exploit=\n", - " number_of_trials_exploit)\n", - "\n", - "end = datetime.datetime.now()\n", - "print(\"done without AP, time: {}, elapsed: {}\".format(end, end-middle))\n", - "\n", - "plot_with_without_ap('mean_results/{}_both_{}_{}.pdf'.format(env_name,\n", - " number_of_tests,\n", - " start),\n", - " metrics_ap, metrics_no_ap)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 100 experiments results\n", - "For 100 experiment, the results are as shown below:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import IFrame\n", - "IFrame(\"mean_results/HandEye3-v0_both_100_2018-10-02_23:02:51.492298.pdf\", width=800, height=600)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can see that Action Planning makes gaining the knowledge, especially knowledge about transitions with block, a bit faster. It is as expected, although in the original article (\"Action-Planning in Anticipatory Classifier System\", Butz, Stolzmann) the results seem a little bit better (meaning faster when using Action Planning in comparison to not using it).\n", - "\n", - "Overall, Action Planning makes gaining the knowledge in HandEye3 environment faster than when not using it. I think that the results for bigger environments could be even better, but it's yet to check." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/source/notebooks/handeye/ACS2 in HandEye.ipynb b/docs/source/notebooks/handeye/ACS2 in HandEye.ipynb index bb62a0b3..9b39aaa9 100644 --- a/docs/source/notebooks/handeye/ACS2 in HandEye.ipynb +++ b/docs/source/notebooks/handeye/ACS2 in HandEye.ipynb @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -45,9 +45,9 @@ "output_type": "stream", "text": [ "\n", - "\u001b[32m#\u001b[0m\u001b[34m■\u001b[0m\u001b[37m□\u001b[0m\n", "\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\n", - "\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\n" + "\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\n", + "\u001b[37m□\u001b[0m\u001b[34m■\u001b[0m\u001b[37m□\u001b[0m\n" ] } ], @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -80,14 +80,14 @@ "output_type": "stream", "text": [ "\n", + "\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\n", "\u001b[37m□\u001b[0m\u001b[34m■\u001b[0m\u001b[37m□\u001b[0m\n", - "\u001b[32m#\u001b[0m\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\n", "\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\u001b[37m□\u001b[0m\n" ] } ], "source": [ - "action = 2 # move South\n", + "action = 0 # move North\n", "\n", "state, reward, done, _ = hand_eye.step(action)\n", "\n", @@ -103,14 +103,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "('w', 'b', 'w', 'g', 'w', 'w', 'w', 'w', 'w', '0')\n" + "('w', 'w', 'w', 'w', 'b', 'w', 'w', 'w', 'w', '2')\n" ] } ], @@ -127,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -243,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -260,9 +260,9 @@ "\n", "from examples.acs2.handeye.utils import calculate_performance\n", "\n", - "TITLE_TEXT_SIZE=24\n", - "AXIS_TEXT_SIZE=18\n", - "LEGEND_TEXT_SIZE=16" + "TITLE_TEXT_SIZE = 24\n", + "AXIS_TEXT_SIZE = 18\n", + "LEGEND_TEXT_SIZE = 16" ] }, { @@ -274,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -313,7 +313,7 @@ " return df\n", "\n", "\n", - "def plot_knowledge(df, ax=None):\n", + "def plot_knowledge(df, ax=None, additional_info=\"\"):\n", " if ax is None:\n", " ax = plt.gca()\n", "\n", @@ -326,12 +326,14 @@ " exploit_df['knowledge'].plot(ax=ax, c='red')\n", " ax.axvline(x=len(explore_df), c='black', linestyle='dashed')\n", "\n", - " ax.set_title(\"Achieved knowledge\", fontsize=TITLE_TEXT_SIZE)\n", + " ax.set_title(\"Achieved knowledge{}\".format(additional_info),\n", + " fontsize=TITLE_TEXT_SIZE)\n", " ax.set_xlabel(\"Trial\", fontsize=AXIS_TEXT_SIZE)\n", " ax.set_ylabel(\"Knowledge [%]\", fontsize=AXIS_TEXT_SIZE)\n", " ax.set_ylim([0, 105])\n", " ax.legend(fontsize=LEGEND_TEXT_SIZE)\n", "\n", + "\n", "def plot_classifiers(df, ax=None):\n", " if ax is None:\n", " ax = plt.gca()\n", @@ -350,13 +352,14 @@ " ax.legend(fontsize=LEGEND_TEXT_SIZE)\n", "\n", "\n", - "def plot_performance(agent, env, metrics_df, cfg, env_name, additional_info):\n", + "def plot_performance(metrics_df, env_name, additional_info,\n", + " with_ap=\"\"):\n", " plt.figure(figsize=(13, 10), dpi=100)\n", " plt.suptitle(f'ACS2 Performance in {env_name} environment '\n", " f'{additional_info}', fontsize=32)\n", "\n", " ax2 = plt.subplot(211)\n", - " plot_knowledge(metrics_df, ax2)\n", + " plot_knowledge(metrics_df, ax2, with_ap)\n", "\n", " ax3 = plt.subplot(212)\n", " plot_classifiers(metrics_df, ax3)\n", @@ -364,8 +367,9 @@ " plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3)\n", "\n", "\n", - "def plot_handeye(env_name='HandEye3-v0', filename='images/handeye.pdf', \n", - " do_action_planning=True):\n", + "def plot_handeye(env_name='HandEye3-v0', filename='results/handeye.pdf',\n", + " do_action_planning=True, number_of_trials_explore=50,\n", + " number_of_trials_exploit=10):\n", " hand_eye = gym.make(env_name)\n", " cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n,\n", " epsilon=1.0,\n", @@ -375,24 +379,26 @@ "\n", " # explore\n", " agent_he = ACS2(cfg)\n", - " population_maze5_explore, metrics_maze5_explore = agent_he.explore(\n", - " hand_eye, 50)\n", + " population_he_explore, metrics_he_explore = agent_he.explore(\n", + " hand_eye, number_of_trials_explore)\n", "\n", " # exploit\n", - " agent_he = ACS2(cfg, population_maze5_explore)\n", - " _, metrics_maze5_exploit = agent_he.exploit(hand_eye, 10)\n", + " agent_he = ACS2(cfg, population_he_explore)\n", + " _, metrics_he_exploit = agent_he.exploit(hand_eye,\n", + " number_of_trials_exploit)\n", "\n", - " maze5_metrics_df = parse_metrics_to_df(metrics_maze5_explore,\n", - " metrics_maze5_exploit)\n", + " he_metrics_df = parse_metrics_to_df(metrics_he_explore,\n", + " metrics_he_exploit)\n", "\n", " if do_action_planning:\n", " message = 'with'\n", " else:\n", " message = 'without'\n", "\n", - " plot_performance(agent_he, hand_eye, maze5_metrics_df, cfg, env_name,\n", + " plot_performance(he_metrics_df, env_name,\n", " '\\n{} Action Planning'.format(message))\n", - " plt.savefig(filename.replace(\" \", \"_\"), format='pdf', dpi=100)" + " # un-comment the below line if you want to save the results to a file\n", + " # plt.savefig(filename.replace(\" \", \"_\"), format='pdf', dpi=100)\n" ] }, { @@ -408,23 +414,35 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": { "scrolled": true }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "env_name = 'HandEye3-v0'\n", + "number_of_trials_explore = 50\n", + "number_of_trials_exploit = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "time start: 2018-09-26 15:54:44.317150\n", - "done with AP, time: 2018-09-26 15:56:27.589234, elapsed: 0:01:43.272084\n", - "done without AP, time: 2018-09-26 15:57:42.879115, elapsed: 0:01:15.289881\n" + "CPU times: user 1min 38s, sys: 76.5 ms, total: 1min 38s\n", + "Wall time: 1min 48s\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -433,10 +451,32 @@ "needs_background": "light" }, "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "plot_handeye(env_name, 'results/{}_ap.pdf'.format(env_name),\n", + " do_action_planning=True,\n", + " number_of_trials_explore=number_of_trials_explore,\n", + " number_of_trials_exploit=number_of_trials_exploit)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 59.8 s, sys: 58.7 ms, total: 59.9 s\n", + "Wall time: 1min 8s\n" + ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -448,35 +488,374 @@ } ], "source": [ - "env_name = 'HandEye3-v0'\n", + "%%time\n", + "plot_handeye(env_name, 'results/{}_no_ap.pdf'.format(env_name),\n", + " do_action_planning=False,\n", + " number_of_trials_explore=number_of_trials_explore,\n", + " number_of_trials_exploit=number_of_trials_exploit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can compare results.\n", + "\n", + "Remember that each trial means 500 steps. In most of the results (not all because of non-deterministic nature of ACS2), we can see that when using Action Planning, we achieve full knowledge (100.0%) about 6-8 trials faster (sometimes more, sometimes less than that). It may seem like a really small difference, but it is actually about 3000-4000 steps.\n", "\n", - "start = datetime.datetime.now()\n", - "print(\"time start: {}\".format(start))\n", + "What is even more important, we achieve knowledge about transitions with block faster than without Action Planning. This is essential, because moves with block are more useful for the real use of the environment.\n", "\n", - "plot_handeye(env_name, 'images/{}_ap.pdf'.format(env_name),\n", - " do_action_planning=True)\n", + "Of course, this is only one sample of a non-deterministic agent. The results shown above are not 'rules'. They are not in any way relevant and do not show even typical behaviour of pyALCS with Action Planning. That's why it's important to see how it works with avarage results of more than just one experiment.\n", "\n", - "middle = datetime.datetime.now()\n", - "print(\"done with AP, time: {}, elapsed: {}\".format(middle, middle-start))\n", + "It can be easily done, but of course will take a lot of time. For me, it took about 4 hours to run 100 experiments of HandEye3-v0 environment, 50 trials each. It will take of course more time for bigger environemnts (like HandEye5-v0 etc.), for more trials and for more experiments. The results are not, however, connected to time. I plotted all the results in a function of the number of trials. Time is irrelevant in this case. \n", "\n", - "plot_handeye(env_name, 'images/{}_no_ap.pdf'.format(env_name),\n", - " do_action_planning=False)\n", + "The results are easily read when they are in the form of a graph. Therefore, I used some functions to plot the numbers I get from running the experiments. They are of course mean values from all 100 experiments.\n", "\n", - "end = datetime.datetime.now()\n", - "print(\"done without AP, time: {}, elapsed: {}\".format(end, end-middle))" + "I used the functions that I mentioned above to plot this, but I needed function to count mean values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now, we can compare results.\n", + "I decided not to save all the results from all 100 experiments and then at the end count the mean, but to count it after each experiment.\n", "\n", - "Remember that each trial means 500 steps. In most of the results (not all because of non-deterministic nature of ACS2), we can see that when using Action Planning, we achieve full knowledge (100.0%) about 6-8 trials faster (sometimes more, sometimes less than that). It may seem like a really small difference, but it is actually about 3000-4000 steps.\n", + "It is counted basically as follows:\n", "\n", - "What is even more important, we achieve knowledge about transitions with block faster than without Action Planning. This is essential, because moves with block are more useful for the real use of the environment.\n", + " mean = (a + b)/2\n", + " mean = (mean * 2 + c)/3\n", + " mean = (mean * 3 + d)/4\n", + " \n", + "And so on." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def mean(i, row_mean, row, first, second):\n", + " return (row_mean[first][second]\n", + " * i + row[first][second]) / (i + 1)\n", + "\n", + "\n", + "def count_mean_values(i: int, metrics, mean_metrics):\n", + " new_metrics = metrics.copy()\n", + " for row, row_new, row_mean in zip(metrics, new_metrics, mean_metrics):\n", + " row_new['performance']['knowledge'] = mean(i, row_mean, row,\n", + " 'performance', 'knowledge')\n", + " row_new['performance']['with_block'] = mean(i, row_mean, row,\n", + " 'performance',\n", + " 'with_block')\n", + " row_new['performance']['no_block'] = mean(i, row_mean, row,\n", + " 'performance', 'no_block')\n", + " row_new['agent']['numerosity'] = mean(i, row_mean, row,\n", + " 'agent', 'numerosity')\n", + " row_new['agent']['steps'] = mean(i, row_mean, row,\n", + " 'agent', 'steps')\n", + " row_new['agent']['reliable'] = mean(i, row_mean, row,\n", + " 'agent', 'reliable')\n", + " return new_metrics\n", + "\n", + "\n", + "def plot_handeye_mean(number_of_tests=50, env_name='HandEye3-v0',\n", + " filename='mean_results/handeye.pdf', do_action_planning=True,\n", + " number_of_trials_explore=50, number_of_trials_exploit=2):\n", + " hand_eye = gym.make(env_name)\n", + " cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n,\n", + " epsilon=1.0,\n", + " do_ga=False,\n", + " do_action_planning=do_action_planning,\n", + " performance_fcn=calculate_performance)\n", + "\n", + " mean_metrics_he_exploit = []\n", + " mean_metrics_he_explore = []\n", + "\n", + " for i in range(number_of_tests):\n", + " # the below line can be un-commented for experiments\n", + " # print(i, datetime.datetime.now())\n", + " \n", + " # explore\n", + " agent_he = ACS2(cfg)\n", + " population_he_explore, metrics_he_explore = agent_he.explore(\n", + " hand_eye, number_of_trials_explore)\n", + "\n", + " # exploit\n", + " agent_he = ACS2(cfg, population_he_explore)\n", + " _, metrics_he_exploit = agent_he.exploit(hand_eye,\n", + " number_of_trials_exploit)\n", + "\n", + " mean_metrics_he_explore = count_mean_values(i, metrics_he_explore,\n", + " mean_metrics_he_explore)\n", + " mean_metrics_he_exploit = count_mean_values(i, metrics_he_exploit,\n", + " mean_metrics_he_exploit)\n", + "\n", + " he_metrics_df = parse_metrics_to_df(mean_metrics_he_explore,\n", + " mean_metrics_he_exploit)\n", + " if do_action_planning:\n", + " with_ap = \", with Action Planning\"\n", + " else:\n", + " with_ap = \", without Action Planning\"\n", + "\n", + " plot_performance(he_metrics_df, env_name,\n", + " '\\nmean for {} experiments'.format(number_of_tests),\n", + " with_ap)\n", + " # un-comment the below line if you want to save the results to a file\n", + " # plt.savefig(filename.replace(\" \", \"_\"), format='pdf', dpi=100)\n", + " return he_metrics_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, I decided to put both results (with Action Planning and without Action Planning) in one pdf document, just so it'll be easily compared. Running all 100 experiments once again would be a waste of time (a lot of time, since it takes 4 hours), so I just use the already counted mean results." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_both_performances(metrics_ap, metrics_no_ap, env_name,\n", + " additional_info):\n", + " plt.figure(figsize=(13, 10), dpi=100)\n", + " plt.suptitle(f'ACS2 Performance in {env_name} environment '\n", + " f'{additional_info}', fontsize=32)\n", + "\n", + " ax2 = plt.subplot(211)\n", + " plot_knowledge(metrics_ap, ax2, \", with Action Planning\")\n", + "\n", + " ax3 = plt.subplot(212)\n", + " plot_knowledge(metrics_no_ap, ax3, \", without Action Planning\")\n", + "\n", + " plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3)\n", + " \n", + "def plot_with_without_ap(filename, metrics_ap, metrics_no_ap):\n", + " plot_both_performances(metrics_ap, metrics_no_ap, env_name,\n", + " '\\nmean for {} experiments'.format(number_of_tests))\n", + " # un-comment the below line if you want to save the results to a file\n", + " # plt.savefig(filename.replace(\" \", \"_\"), format='pdf', dpi=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, I was ready to run the experiments. Since it took 4 hours, I do not recommend running it when you don't have enough time. You can do a test with 2 experiments (less than 10 minutes), just as I did below. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "env_name = 'HandEye3-v0'\n", + "number_of_tests = 2\n", + "number_of_trials_explore = 50\n", + "number_of_trials_exploit = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2min 33s, sys: 38.5 ms, total: 2min 33s\n", + "Wall time: 2min 40s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "metrics_ap = plot_handeye_mean(number_of_tests, env_name,\n", + " 'mean_results/{}_ap_{}.pdf'.\n", + " format(env_name, number_of_tests),\n", + " do_action_planning=True,\n", + " number_of_trials_explore=\n", + " number_of_trials_explore,\n", + " number_of_trials_exploit=\n", + " number_of_trials_exploit)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2min 12s, sys: 498 ms, total: 2min 12s\n", + "Wall time: 2min 20s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "metrics_no_ap = plot_handeye_mean(number_of_tests, env_name,\n", + " 'mean_results/{}_no_ap_{}.pdf'.\n", + " format(env_name, number_of_tests),\n", + " do_action_planning=False,\n", + " number_of_trials_explore=\n", + " number_of_trials_explore,\n", + " number_of_trials_exploit=\n", + " number_of_trials_exploit)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_with_without_ap('mean_results/{}_both_{}_{}.pdf'.format(env_name,\n", + " number_of_tests,\n", + " start),\n", + " metrics_ap, metrics_no_ap)\n", + "\n", + "# un-comment these lines to save the results to a csv file\n", + "# metrics_ap.to_csv('mean_results/{}_ap_{}_{}.csv'.\n", + "# format(env_name, number_of_tests, start).\n", + "# replace(' ', '_'))\n", + "\n", + "# metrics_no_ap.to_csv('mean_results/{}_no_ap_{}_{}.txt'.\n", + "# format(env_name, number_of_tests, start).\n", + "# replace(' ', '_'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bigger experiments results\n", + "For 100 experiments in HandEye3 environment, the results are as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import IFrame\n", + "IFrame(\"mean_results/HandEye3-v0_both_100_2018-10-02_23:02:51.492298.pdf\", width=800, height=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I also did experiments for HandEye4, but only 50 experiments (100 trials each), because it already took about 16,5 hours. Here the result is even bigger - you can actually see that the knowledge about moves with block is growing faster with Action Planning." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "IFrame(\"mean_results/HandEye4-v0_both_50_2018-10-03_22:55:48.735472.pdf\", width=800, height=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see that Action Planning makes gaining the knowledge, especially knowledge about transitions with block, a bit faster. It is as expected, although in the original article (\"Action-Planning in Anticipatory Classifier System\", Butz, Stolzmann) the results seem a little bit better (meaning faster when using Action Planning in comparison to not using it).\n", "\n", - "Of course, this is only one sample of a non-deterministic agent. The results shown above are not 'rules'. They are not in any way relevant and do not show even typical behaviour of pyALCS with Action Planning." + "Overall, Action Planning makes gaining the knowledge in HandEye environment faster than when not using it. 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zSTO$z18@MhR)2>fQ1ck-_a6=jFE46p`VGbhOzywI_#l6l1MK+k^g;L#fA9|i^z9G+ z@$m!J`ZpZ>aNt(g?=T+Jbb$K(k7j;8;O%!9KWYyAr5xbAqh`L}VDLZr2jTe(j2|$r zztx4J76QM+cmRX_I}GJu{RV?UQL`iJ_g_DtFeJ|(^uc)l#0%p`S*%~{!Xbawg#$?b zD2F=C_`NRAA7cU7G62>$zm-D(^T8i5ILc)GN(+MLPd+1|e~eWq5{|Mjzt#o(**|db zKvA~q*K)wH{9Wcy9>gDd1?AyInUvq^^7H>84_;s`_j@^D%>1rvP+kPe%>UXRFA{Ld ze~0k`OVwXtFkle=M2oAlk(I5P^I3pdIeT*lz!d@}W59T-J2;@`@Uv$PD{nJk5=2dm YF0Mw-u4mICFop2)<1jHvsYv7eKXbp4W&i*H literal 0 HcmV?d00001 From 360387ca0f064e91a395fe12c82d217ccda92d48 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 10 Oct 2018 10:37:07 +0200 Subject: [PATCH 81/83] moved HandEye notebook to notebooks dir --- .../{handeye => }/ACS2 in HandEye.ipynb | 20 +- ...v0_both_100_2018-10-02_23:02:51.492298.pdf | Bin 20235 -> 0 bytes ...-v0_both_50_2018-10-03_22:55:48.735472.pdf | Bin 22216 -> 0 bytes ...-v0_both_25_2018-10-04_23:02:07.252081.pdf | Bin 22199 -> 0 bytes .../notebooks/handeye/mean_results_handeye.py | 237 ------------------ docs/source/notebooks/handeye/plot_handeye.py | 164 ------------ 6 files changed, 10 insertions(+), 411 deletions(-) rename docs/source/notebooks/{handeye => }/ACS2 in HandEye.ipynb (99%) delete mode 100644 docs/source/notebooks/handeye/mean_results/HandEye3-v0_both_100_2018-10-02_23:02:51.492298.pdf delete mode 100644 docs/source/notebooks/handeye/mean_results/HandEye4-v0_both_50_2018-10-03_22:55:48.735472.pdf delete mode 100644 docs/source/notebooks/handeye/mean_results/HandEye5-v0_both_25_2018-10-04_23:02:07.252081.pdf delete mode 100644 docs/source/notebooks/handeye/mean_results_handeye.py delete mode 100644 docs/source/notebooks/handeye/plot_handeye.py diff --git a/docs/source/notebooks/handeye/ACS2 in HandEye.ipynb b/docs/source/notebooks/ACS2 in HandEye.ipynb similarity index 99% rename from docs/source/notebooks/handeye/ACS2 in HandEye.ipynb rename to docs/source/notebooks/ACS2 in HandEye.ipynb index 9b39aaa9..3d1ab9d0 100644 --- a/docs/source/notebooks/handeye/ACS2 in HandEye.ipynb +++ b/docs/source/notebooks/ACS2 in HandEye.ipynb @@ -781,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -791,24 +791,24 @@ " \n", " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import IFrame\n", - "IFrame(\"mean_results/HandEye3-v0_both_100_2018-10-02_23:02:51.492298.pdf\", width=800, height=600)" + "IFrame(\"temp/HandEye3-v0_both_100.pdf\", width=800, height=600)" ] }, { @@ -820,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -830,23 +830,23 @@ " \n", " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "IFrame(\"mean_results/HandEye4-v0_both_50_2018-10-03_22:55:48.735472.pdf\", width=800, height=600)" + "IFrame(\"temp/HandEye4-v0_both_50.pdf\", width=800, height=600)" ] }, { diff --git a/docs/source/notebooks/handeye/mean_results/HandEye3-v0_both_100_2018-10-02_23:02:51.492298.pdf b/docs/source/notebooks/handeye/mean_results/HandEye3-v0_both_100_2018-10-02_23:02:51.492298.pdf deleted file mode 100644 index 5c4d3cda0d2b0eed7ac4de00867c6b478c502dc8..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 20235 zcmb`v1yokg^EgaODkUH#4-E=D4Lo#rw-VCb-2xJVbV`Gughh#nfKt*R5>lcdAuWOe zD)PS%=;tfQ`5n%C-shY>_uhGScV=g1XJ+T_UN&`USso}af{^XOBZaC~?jRTd(FB3DeO)X;U@21%QwL{jkfEmfD8aU(y~DRmOdUJu!19iQ}Xv!>i1Ov zWCQ|h*_c|`I$4AG4x8(%nb}#Idw}>4I{zOzPzV6{GXM!ECua|L00q!q3LwSO33x&s zb|B~G?CAnR4^v7D1eUh+vNgBVlmk!$Pugy#PVO$IZkA5wzJEr3SRX(HRKgoBuM0jY&r{6I`8bQZl_;)XM|B_ohfyN5Z5|MrvoQp_w(n<+4Q_~ zLB3CG=Qw`-s1JDYGehh>Z_sYs{uBfKi$H(pTYT<(l{sytkO6U8Zvl?B$?+Ro(6gIG6-MMN{O)a|Rd``6H3FY+r zfp;d}_CmYD0o@2O#>T*gfK9ca4JY3{?Ed*D)gL4;e+zK3PfaK!;f^lZ)&Ig1KZTAzCj0n#NK_Rl6kXy?rY{-h_A}S2fWWy-hA>bfB!i%)8H1%!>dj_ zMw_%8kK2ZyoKY~AeSBjt;_Zz55_v!em*um0q>AzVTBMLlR;iiakF$@5+jY#`c% z;oeidQ#~B1X)hwQsQfl3fkCt3Q?j<+Rv+GbpYK=AvPcw*?>du&WbAy4wC!>GHjv@8BfnzLL}uvpWY(5Nh!OSR%~ejv?TRd?T@Y?k@@(jtBHkrR z>I_w5kE*Xgxi@$UnlPwoZWj?XxM=|UM1=FFzIN;COwN4MP2H!Tl3 zXp4N_r{RMz{6FN4XJ|9iGp1j7LKtw<%GtFy)$pnr?zHqE6

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ze~0k`OVwXtFkle=M2oAlk(I5P^I3pdIeT*lz!d@}W59T-J2;@`@Uv$PD{nJk5=2dm YF0Mw-u4mICFop2)<1jHvsYv7eKXbp4W&i*H diff --git a/docs/source/notebooks/handeye/mean_results_handeye.py b/docs/source/notebooks/handeye/mean_results_handeye.py deleted file mode 100644 index 7149cc58..00000000 --- a/docs/source/notebooks/handeye/mean_results_handeye.py +++ /dev/null @@ -1,237 +0,0 @@ -# Plot constants -import datetime - -import gym -import gym_handeye - -from lcs.agents.acs2 import ACS2, ClassifiersList, Configuration -import numpy as np -import pandas as pd -import matplotlib -import matplotlib.pyplot as plt - -from examples.acs2.handeye.utils import calculate_performance - -TITLE_TEXT_SIZE = 24 -AXIS_TEXT_SIZE = 18 -LEGEND_TEXT_SIZE = 16 - - -def parse_metrics_to_df(explore_metrics, exploit_metrics): - def extract_details(row): - row['trial'] = row['agent']['trial'] - row['steps'] = row['agent']['steps'] - row['numerosity'] = row['agent']['numerosity'] - row['reliable'] = row['agent']['reliable'] - row['knowledge'] = row['performance']['knowledge'] - row['with_block'] = row['performance']['with_block'] - row['no_block'] = row['performance']['no_block'] - return row - - # Load both metrics into data frame - explore_df = pd.DataFrame(explore_metrics) - exploit_df = pd.DataFrame(exploit_metrics) - - # Mark them with specific phase - explore_df['phase'] = 'explore' - exploit_df['phase'] = 'exploit' - - # Extract details - explore_df = explore_df.apply(extract_details, axis=1) - exploit_df = exploit_df.apply(extract_details, axis=1) - - # Adjuts exploit trial counter - exploit_df['trial'] = exploit_df.apply( - lambda r: r['trial'] + len(explore_df), axis=1) - - # Concatenate both dataframes - df = pd.concat([explore_df, exploit_df]) - df.drop(['agent', 'environment', 'performance'], axis=1, inplace=True) - df.set_index('trial', inplace=True) - - return df - - -def plot_knowledge(df, ax=None, additional_info=""): - if ax is None: - ax = plt.gca() - - explore_df = df.query("phase == 'explore'") - exploit_df = df.query("phase == 'exploit'") - - explore_df['knowledge'].plot(ax=ax, c='blue') - explore_df['with_block'].plot(ax=ax, c='green') - explore_df['no_block'].plot(ax=ax, c='yellow') - exploit_df['knowledge'].plot(ax=ax, c='red') - ax.axvline(x=len(explore_df), c='black', linestyle='dashed') - - ax.set_title("Achieved knowledge{}".format(additional_info), - fontsize=TITLE_TEXT_SIZE) - ax.set_xlabel("Trial", fontsize=AXIS_TEXT_SIZE) - ax.set_ylabel("Knowledge [%]", fontsize=AXIS_TEXT_SIZE) - ax.set_ylim([0, 105]) - ax.legend(fontsize=LEGEND_TEXT_SIZE) - - -def plot_classifiers(df, ax=None): - if ax is None: - ax = plt.gca() - - explore_df = df.query("phase == 'explore'") - exploit_df = df.query("phase == 'exploit'") - - df['numerosity'].plot(ax=ax, c='blue') - df['reliable'].plot(ax=ax, c='red') - - ax.axvline(x=len(explore_df), c='black', linestyle='dashed') - - ax.set_title("Classifiers", fontsize=TITLE_TEXT_SIZE) - ax.set_xlabel("Trial", fontsize=AXIS_TEXT_SIZE) - ax.set_ylabel("Classifiers", fontsize=AXIS_TEXT_SIZE) - ax.legend(fontsize=LEGEND_TEXT_SIZE) - - -def plot_performance(metrics_df, env_name, additional_info, - with_ap=""): - plt.figure(figsize=(13, 10), dpi=100) - plt.suptitle(f'ACS2 Performance in {env_name} environment ' - f'{additional_info}', fontsize=32) - - ax2 = plt.subplot(211) - plot_knowledge(metrics_df, ax2, with_ap) - - ax3 = plt.subplot(212) - plot_classifiers(metrics_df, ax3) - - plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3) - - -def plot_both_performances(metrics_ap, metrics_no_ap, env_name, - additional_info): - plt.figure(figsize=(13, 10), dpi=100) - plt.suptitle(f'ACS2 Performance in {env_name} environment ' - f'{additional_info}', fontsize=32) - - ax2 = plt.subplot(211) - plot_knowledge(metrics_ap, ax2, ", with Action Planning") - - ax3 = plt.subplot(212) - plot_knowledge(metrics_no_ap, ax3, ", without Action Planning") - - plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3) - - -def mean(i, row_mean, row, first, second): - return (row_mean[first][second] * i + row[first][second]) / (i + 1) - - -def count_mean_values(i: int, metrics, mean_metrics): - new_metrics = metrics.copy() - for row, row_new, row_mean in zip(metrics, new_metrics, mean_metrics): - row_new['performance']['knowledge'] = mean(i, row_mean, row, - 'performance', 'knowledge') - row_new['performance']['with_block'] = mean(i, row_mean, row, - 'performance', - 'with_block') - row_new['performance']['no_block'] = mean(i, row_mean, row, - 'performance', 'no_block') - row_new['agent']['numerosity'] = mean(i, row_mean, row, - 'agent', 'numerosity') - row_new['agent']['steps'] = mean(i, row_mean, row, - 'agent', 'steps') - row_new['agent']['reliable'] = mean(i, row_mean, row, - 'agent', 'reliable') - return new_metrics - - -def plot_handeye_mean(number_of_tests=50, env_name='HandEye3-v0', - filename='mean_results/handeye.pdf', - do_action_planning=True, - number_of_trials_explore=50, - number_of_trials_exploit=10): - hand_eye = gym.make(env_name) - cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, - epsilon=1.0, - do_ga=False, - do_action_planning=do_action_planning, - performance_fcn=calculate_performance) - - mean_metrics_he_exploit = [] - mean_metrics_he_explore = [] - - for i in range(number_of_tests): - # the below line can be un-commented for experiments - # print(i, datetime.datetime.now()) - - # explore - agent_he = ACS2(cfg) - population_he_explore, metrics_he_explore = agent_he.explore( - hand_eye, number_of_trials_explore) - - # exploit - agent_he = ACS2(cfg, population_he_explore) - _, metrics_he_exploit = agent_he.exploit(hand_eye, - number_of_trials_exploit) - - mean_metrics_he_explore = count_mean_values(i, metrics_he_explore, - mean_metrics_he_explore) - mean_metrics_he_exploit = count_mean_values(i, metrics_he_exploit, - mean_metrics_he_exploit) - - he_metrics_df = parse_metrics_to_df(mean_metrics_he_explore, - mean_metrics_he_exploit) - if do_action_planning: - with_ap = ", with Action Planning" - else: - with_ap = ", without Action Planning" - - plot_performance(he_metrics_df, env_name, - '\nmean for {} experiments'.format(number_of_tests), - with_ap) - plt.savefig(filename.replace(" ", "_"), format='pdf', dpi=100) - return he_metrics_df - - -def plot_with_without_ap(filename, metrics_ap, metrics_no_ap): - plot_both_performances(metrics_ap, metrics_no_ap, env_name, - '\nmean for {} experiments'.format(number_of_tests)) - plt.savefig(filename.replace(" ", "_"), format='pdf', dpi=100) - - -if __name__ == "__main__": - env_name = 'HandEye3-v0' - number_of_tests = 100 - number_of_trials_explore = 50 - number_of_trials_exploit = 10 - - start = datetime.datetime.now() - print("time start: {}".format(start)) - - metrics_ap = plot_handeye_mean( - number_of_tests, env_name, 'mean_results/{}_ap_{}_{}.pdf' - .format(env_name, number_of_tests, start), do_action_planning=True, - number_of_trials_explore=number_of_trials_explore, - number_of_trials_exploit=number_of_trials_exploit) - - middle = datetime.datetime.now() - print("done with AP, time: {}, elapsed: {}".format(middle, middle - start)) - - metrics_no_ap = plot_handeye_mean( - number_of_tests, env_name, 'mean_results/{}_no_ap_{}_{}.pdf'. - format(env_name, number_of_tests, start), do_action_planning=False, - number_of_trials_explore=number_of_trials_explore, - number_of_trials_exploit=number_of_trials_exploit) - - end = datetime.datetime.now() - print("done without AP, time: {}, elapsed: {}".format(end, end - middle)) - - plot_with_without_ap('mean_results/{}_both_{}_{}.pdf'.format( - env_name, number_of_tests, start), metrics_ap, metrics_no_ap) - - metrics_ap.to_csv('mean_results/{}_ap_{}_{}.csv'. - format(env_name, number_of_tests, start). - replace(' ', '_')) - - metrics_no_ap.to_csv('mean_results/{}_no_ap_{}_{}.txt'. - format(env_name, number_of_tests, start). - replace(' ', '_')) diff --git a/docs/source/notebooks/handeye/plot_handeye.py b/docs/source/notebooks/handeye/plot_handeye.py deleted file mode 100644 index 96033346..00000000 --- a/docs/source/notebooks/handeye/plot_handeye.py +++ /dev/null @@ -1,164 +0,0 @@ -# Plot constants -import datetime - -import gym -import gym_handeye - -from lcs.agents.acs2 import ACS2, ClassifiersList, Configuration -import numpy as np -import pandas as pd -import matplotlib -import matplotlib.pyplot as plt - -from examples.acs2.handeye.utils import calculate_performance - -TITLE_TEXT_SIZE = 24 -AXIS_TEXT_SIZE = 18 -LEGEND_TEXT_SIZE = 16 - - -def parse_metrics_to_df(explore_metrics, exploit_metrics): - def extract_details(row): - row['trial'] = row['agent']['trial'] - row['steps'] = row['agent']['steps'] - row['numerosity'] = row['agent']['numerosity'] - row['reliable'] = row['agent']['reliable'] - row['knowledge'] = row['performance']['knowledge'] - row['with_block'] = row['performance']['with_block'] - row['no_block'] = row['performance']['no_block'] - return row - - # Load both metrics into data frame - explore_df = pd.DataFrame(explore_metrics) - exploit_df = pd.DataFrame(exploit_metrics) - - # Mark them with specific phase - explore_df['phase'] = 'explore' - exploit_df['phase'] = 'exploit' - - # Extract details - explore_df = explore_df.apply(extract_details, axis=1) - exploit_df = exploit_df.apply(extract_details, axis=1) - - # Adjuts exploit trial counter - exploit_df['trial'] = exploit_df.apply( - lambda r: r['trial'] + len(explore_df), axis=1) - - # Concatenate both dataframes - df = pd.concat([explore_df, exploit_df]) - df.drop(['agent', 'environment', 'performance'], axis=1, inplace=True) - df.set_index('trial', inplace=True) - - return df - - -def plot_knowledge(df, ax=None, additional_info=""): - if ax is None: - ax = plt.gca() - - explore_df = df.query("phase == 'explore'") - exploit_df = df.query("phase == 'exploit'") - - explore_df['knowledge'].plot(ax=ax, c='blue') - explore_df['with_block'].plot(ax=ax, c='green') - explore_df['no_block'].plot(ax=ax, c='yellow') - exploit_df['knowledge'].plot(ax=ax, c='red') - ax.axvline(x=len(explore_df), c='black', linestyle='dashed') - - ax.set_title("Achieved knowledge{}".format(additional_info), - fontsize=TITLE_TEXT_SIZE) - ax.set_xlabel("Trial", fontsize=AXIS_TEXT_SIZE) - ax.set_ylabel("Knowledge [%]", fontsize=AXIS_TEXT_SIZE) - ax.set_ylim([0, 105]) - ax.legend(fontsize=LEGEND_TEXT_SIZE) - - -def plot_classifiers(df, ax=None): - if ax is None: - ax = plt.gca() - - explore_df = df.query("phase == 'explore'") - exploit_df = df.query("phase == 'exploit'") - - df['numerosity'].plot(ax=ax, c='blue') - df['reliable'].plot(ax=ax, c='red') - - ax.axvline(x=len(explore_df), c='black', linestyle='dashed') - - ax.set_title("Classifiers", fontsize=TITLE_TEXT_SIZE) - ax.set_xlabel("Trial", fontsize=AXIS_TEXT_SIZE) - ax.set_ylabel("Classifiers", fontsize=AXIS_TEXT_SIZE) - ax.legend(fontsize=LEGEND_TEXT_SIZE) - - -def plot_performance(metrics_df, env_name, additional_info, - with_ap=""): - plt.figure(figsize=(13, 10), dpi=100) - plt.suptitle(f'ACS2 Performance in {env_name} environment ' - f'{additional_info}', fontsize=32) - - ax2 = plt.subplot(211) - plot_knowledge(metrics_df, ax2, with_ap) - - ax3 = plt.subplot(212) - plot_classifiers(metrics_df, ax3) - - plt.subplots_adjust(top=0.86, wspace=0.3, hspace=0.3) - - -def plot_handeye(env_name='HandEye3-v0', filename='results/handeye.pdf', - do_action_planning=True, number_of_trials_explore=50, - number_of_trials_exploit=10): - hand_eye = gym.make(env_name) - cfg = Configuration(hand_eye.observation_space.n, hand_eye.action_space.n, - epsilon=1.0, - do_ga=False, - do_action_planning=do_action_planning, - performance_fcn=calculate_performance) - - # explore - agent_he = ACS2(cfg) - population_he_explore, metrics_he_explore = agent_he.explore( - hand_eye, number_of_trials_explore) - - # exploit - agent_he = ACS2(cfg, population_he_explore) - _, metrics_he_exploit = agent_he.exploit(hand_eye, - number_of_trials_exploit) - - he_metrics_df = parse_metrics_to_df(metrics_he_explore, - metrics_he_exploit) - - if do_action_planning: - message = 'with' - else: - message = 'without' - - plot_performance(he_metrics_df, env_name, - '\n{} Action Planning'.format(message)) - plt.savefig(filename.replace(" ", "_"), format='pdf', dpi=100) - - -if __name__ == "__main__": - env_name = 'HandEye3-v0' - number_of_trials_explore = 50 - number_of_trials_exploit = 10 - - start = datetime.datetime.now() - print("time start: {}".format(start)) - - plot_handeye(env_name, 'results/{}_ap_{}.pdf'.format(env_name, start), - do_action_planning=True, - number_of_trials_explore=number_of_trials_explore, - number_of_trials_exploit=number_of_trials_exploit) - - middle = datetime.datetime.now() - print("done with AP, time: {}, elapsed: {}".format(middle, middle - start)) - - plot_handeye(env_name, 'results/{}_no_ap_{}.pdf'.format(env_name, start), - do_action_planning=False, - number_of_trials_explore=number_of_trials_explore, - number_of_trials_exploit=number_of_trials_exploit) - - end = datetime.datetime.now() - print("done without AP, time: {}, elapsed: {}".format(end, end - middle)) From 69f6cbca2eae5afefca66eebc8a92d54165671a0 Mon Sep 17 00:00:00 2001 From: e-dzia Date: Wed, 10 Oct 2018 11:03:39 +0200 Subject: [PATCH 82/83] added important pdfs --- .../notebooks/temp/HandEye3-v0_both_100.pdf | Bin 0 -> 20235 bytes .../notebooks/temp/HandEye4-v0_both_50.pdf | Bin 0 -> 22216 bytes .../notebooks/temp/HandEye5-v0_both_25.pdf | Bin 0 -> 22199 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/source/notebooks/temp/HandEye3-v0_both_100.pdf create mode 100644 docs/source/notebooks/temp/HandEye4-v0_both_50.pdf create mode 100644 docs/source/notebooks/temp/HandEye5-v0_both_25.pdf diff --git a/docs/source/notebooks/temp/HandEye3-v0_both_100.pdf b/docs/source/notebooks/temp/HandEye3-v0_both_100.pdf new file 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