From 2355e0b862b7bf407c83ae286ade99746e1074c5 Mon Sep 17 00:00:00 2001 From: Ashvin Nair Date: Thu, 19 Aug 2021 14:34:11 -0700 Subject: [PATCH 01/12] Working SAC test --- .gitignore | 1 + rlkit/core/rl_algorithm.py | 12 +- rlkit/envs/proxy_env.py | 5 +- rlkit/envs/wrappers.py | 3 + rlkit/launchers/launcher_util.py | 2 + rlkit/testing/__init__.py | 0 rlkit/testing/csv_util.py | 26 + rlkit/testing/np_test_case.py | 99 + rlkit/testing/stub_classes.py | 103 + rlkit/testing/testing_utils.py | 52 + rlkit/testing/tf_test_case.py | 40 + tests/regression/__init__.py | 0 tests/regression/awac/__init__.py | 0 tests/regression/awac/d4rl/__init__.py | 0 tests/regression/awac/d4rl/id0/debug.log | 1779 ++ .../awac/d4rl/id0/pretrain_policy.csv | 0 tests/regression/awac/d4rl/id0/pretrain_q.csv | 0 tests/regression/awac/d4rl/id0/progress.csv | 6 + .../regression/awac/d4rl/id0/script_name.txt | 1 + tests/regression/awac/d4rl/id0/variant.json | 96 + tests/regression/awac/d4rl/test_d4rl.py | 29 + tests/regression/awac/gcrl/__init__.py | 0 .../awac/gcrl/gcrl_data_mini/id0/debug.log | 19800 ++++++++++++++++ .../gcrl_data_mini/id0/pretrain_policy.csv | 11 + .../awac/gcrl/gcrl_data_mini/id0/progress.csv | 101 + .../gcrl/gcrl_data_mini/id0/script_name.txt | 1 + .../awac/gcrl/gcrl_data_mini/id0/variant.json | 156 + .../gcrl/gcrl_data_mini/id0/video_0_vae.p | Bin 0 -> 511525 bytes tests/regression/awac/gcrl/id0/debug.log | 2079 ++ .../awac/gcrl/id0/pretrain_policy.csv | 0 tests/regression/awac/gcrl/id0/pretrain_q.csv | 2 + tests/regression/awac/gcrl/id0/progress.csv | 6 + .../regression/awac/gcrl/id0/script_name.txt | 1 + tests/regression/awac/gcrl/id0/variant.json | 135 + .../awac/gcrl/id0_offline/debug.log | 216 + .../awac/gcrl/id0_offline/pretrain_policy.csv | 0 .../awac/gcrl/id0_offline/pretrain_q.csv | 2 + .../awac/gcrl/id0_offline/progress.csv | 0 .../awac/gcrl/id0_offline/script_name.txt | 1 + .../awac/gcrl/id0_offline/variant.json | 111 + .../awac/gcrl/test_awac_gcrl_offline.py | 179 + .../awac/gcrl/test_awac_gcrl_online.py | 37 + tests/regression/awac/hand/__init__.py | 0 tests/regression/awac/hand/id0/debug.log | 528 + .../awac/hand/id0/pretrain_policy.csv | 0 tests/regression/awac/hand/id0/pretrain_q.csv | 2 + tests/regression/awac/hand/id0/progress.csv | 6 + .../regression/awac/hand/id0/script_name.txt | 1 + tests/regression/awac/hand/id0/variant.json | 106 + .../awac/hand/id0_offline/debug.log | 528 + .../awac/hand/id0_offline/pretrain_policy.csv | 0 .../awac/hand/id0_offline/pretrain_q.csv | 2 + .../awac/hand/id0_offline/progress.csv | 6 + .../awac/hand/id0_offline/script_name.txt | 1 + .../awac/hand/id0_offline/variant.json | 110 + tests/regression/awac/hand/pen2_sparse.npy | Bin 0 -> 3572683 bytes tests/regression/awac/hand/test_awac_hand.py | 53 + .../awac/hand/test_awac_hand_offline.py | 168 + tests/regression/awac/mujoco/__init__.py | 0 .../awac/mujoco/hc_action_noise_15.npy | Bin 0 -> 6033949 bytes tests/regression/awac/mujoco/id0/debug.log | 7238 ++++++ .../awac/mujoco/id0/pretrain_policy.csv | 0 .../regression/awac/mujoco/id0/pretrain_q.csv | 2 + tests/regression/awac/mujoco/id0/progress.csv | 6 + .../awac/mujoco/id0/script_name.txt | 1 + tests/regression/awac/mujoco/id0/variant.json | 100 + .../awac/mujoco/id0_offline/debug.log | 523 + .../awac/mujoco/id0_offline/pretrain_q.csv | 2 + .../awac/mujoco/id0_offline/progress.csv | 6 + .../awac/mujoco/id0_offline/script_name.txt | 1 + .../awac/mujoco/id0_offline/variant.json | 104 + .../awac/mujoco/test_awac_mujoco.py | 37 + .../awac/mujoco/test_awac_mujoco_offline.py | 148 + .../debug.log | 190 + .../progress.csv | 3 + .../script_name.txt | 1 + .../variant.json | 28 + tests/regression/sac/__init__.py | 0 tests/regression/sac/test_sac.py | 130 + tests/regression/val/__init__.py | 0 tests/regression/val/id0_pretrained/debug.log | 859 + .../val/id0_pretrained/pretrain_policy.csv | 0 .../val/id0_pretrained/pretrain_q.csv | 2 + .../val/id0_pretrained/progress.csv | 6 + .../val/id0_pretrained/script_name.txt | 1 + .../val/id0_pretrained/variant.json | 224 + tests/regression/val/id0_vqvae/debug.log | 114 + .../val/id0_vqvae/pixelcnn_progress.csv | 2 + tests/regression/val/id0_vqvae/progress.csv | 0 .../regression/val/id0_vqvae/script_name.txt | 1 + .../regression/val/id0_vqvae/test_recon_0.png | Bin 0 -> 10873 bytes .../val/id0_vqvae/train_recon_0.png | Bin 0 -> 11916 bytes .../regression/val/id0_vqvae/vae_progress.csv | 2 + tests/regression/val/id0_vqvae/variant.json | 74 + tests/regression/val/test_train_vqvae.py | 32 + tests/regression/val/test_val_pretrained.py | 307 + 96 files changed, 36740 insertions(+), 7 deletions(-) create mode 100644 rlkit/testing/__init__.py create mode 100644 rlkit/testing/csv_util.py create mode 100644 rlkit/testing/np_test_case.py create mode 100644 rlkit/testing/stub_classes.py create mode 100644 rlkit/testing/testing_utils.py create mode 100644 rlkit/testing/tf_test_case.py create mode 100644 tests/regression/__init__.py create mode 100644 tests/regression/awac/__init__.py create mode 100644 tests/regression/awac/d4rl/__init__.py create mode 100644 tests/regression/awac/d4rl/id0/debug.log create mode 100644 tests/regression/awac/d4rl/id0/pretrain_policy.csv create mode 100644 tests/regression/awac/d4rl/id0/pretrain_q.csv create mode 100644 tests/regression/awac/d4rl/id0/progress.csv create mode 100644 tests/regression/awac/d4rl/id0/script_name.txt create mode 100644 tests/regression/awac/d4rl/id0/variant.json create mode 100644 tests/regression/awac/d4rl/test_d4rl.py create mode 100644 tests/regression/awac/gcrl/__init__.py create mode 100644 tests/regression/awac/gcrl/gcrl_data_mini/id0/debug.log create mode 100644 tests/regression/awac/gcrl/gcrl_data_mini/id0/pretrain_policy.csv create mode 100644 tests/regression/awac/gcrl/gcrl_data_mini/id0/progress.csv create mode 100644 tests/regression/awac/gcrl/gcrl_data_mini/id0/script_name.txt create mode 100644 tests/regression/awac/gcrl/gcrl_data_mini/id0/variant.json create mode 100644 tests/regression/awac/gcrl/gcrl_data_mini/id0/video_0_vae.p create mode 100644 tests/regression/awac/gcrl/id0/debug.log create mode 100644 tests/regression/awac/gcrl/id0/pretrain_policy.csv create mode 100644 tests/regression/awac/gcrl/id0/pretrain_q.csv create mode 100644 tests/regression/awac/gcrl/id0/progress.csv create mode 100644 tests/regression/awac/gcrl/id0/script_name.txt create mode 100644 tests/regression/awac/gcrl/id0/variant.json create mode 100644 tests/regression/awac/gcrl/id0_offline/debug.log create mode 100644 tests/regression/awac/gcrl/id0_offline/pretrain_policy.csv create mode 100644 tests/regression/awac/gcrl/id0_offline/pretrain_q.csv create mode 100644 tests/regression/awac/gcrl/id0_offline/progress.csv create mode 100644 tests/regression/awac/gcrl/id0_offline/script_name.txt create mode 100644 tests/regression/awac/gcrl/id0_offline/variant.json create mode 100644 tests/regression/awac/gcrl/test_awac_gcrl_offline.py create mode 100644 tests/regression/awac/gcrl/test_awac_gcrl_online.py create mode 100644 tests/regression/awac/hand/__init__.py create mode 100644 tests/regression/awac/hand/id0/debug.log create mode 100644 tests/regression/awac/hand/id0/pretrain_policy.csv create mode 100644 tests/regression/awac/hand/id0/pretrain_q.csv create mode 100644 tests/regression/awac/hand/id0/progress.csv create mode 100644 tests/regression/awac/hand/id0/script_name.txt create mode 100644 tests/regression/awac/hand/id0/variant.json create mode 100644 tests/regression/awac/hand/id0_offline/debug.log create mode 100644 tests/regression/awac/hand/id0_offline/pretrain_policy.csv create mode 100644 tests/regression/awac/hand/id0_offline/pretrain_q.csv create mode 100644 tests/regression/awac/hand/id0_offline/progress.csv create mode 100644 tests/regression/awac/hand/id0_offline/script_name.txt create mode 100644 tests/regression/awac/hand/id0_offline/variant.json create mode 100644 tests/regression/awac/hand/pen2_sparse.npy create mode 100644 tests/regression/awac/hand/test_awac_hand.py create mode 100644 tests/regression/awac/hand/test_awac_hand_offline.py create mode 100644 tests/regression/awac/mujoco/__init__.py create mode 100644 tests/regression/awac/mujoco/hc_action_noise_15.npy create mode 100644 tests/regression/awac/mujoco/id0/debug.log create mode 100644 tests/regression/awac/mujoco/id0/pretrain_policy.csv create mode 100644 tests/regression/awac/mujoco/id0/pretrain_q.csv create mode 100644 tests/regression/awac/mujoco/id0/progress.csv create mode 100644 tests/regression/awac/mujoco/id0/script_name.txt create mode 100644 tests/regression/awac/mujoco/id0/variant.json create mode 100644 tests/regression/awac/mujoco/id0_offline/debug.log create mode 100644 tests/regression/awac/mujoco/id0_offline/pretrain_q.csv create mode 100644 tests/regression/awac/mujoco/id0_offline/progress.csv create mode 100644 tests/regression/awac/mujoco/id0_offline/script_name.txt create mode 100644 tests/regression/awac/mujoco/id0_offline/variant.json create mode 100644 tests/regression/awac/mujoco/test_awac_mujoco.py create mode 100644 tests/regression/awac/mujoco/test_awac_mujoco_offline.py create mode 100644 tests/regression/sac/21-08-18-default_2021_08_18_14_36_11_id478649--s977983/debug.log create mode 100644 tests/regression/sac/21-08-18-default_2021_08_18_14_36_11_id478649--s977983/progress.csv create mode 100644 tests/regression/sac/21-08-18-default_2021_08_18_14_36_11_id478649--s977983/script_name.txt create mode 100644 tests/regression/sac/21-08-18-default_2021_08_18_14_36_11_id478649--s977983/variant.json create mode 100644 tests/regression/sac/__init__.py create mode 100644 tests/regression/sac/test_sac.py create mode 100644 tests/regression/val/__init__.py create mode 100644 tests/regression/val/id0_pretrained/debug.log create mode 100644 tests/regression/val/id0_pretrained/pretrain_policy.csv create mode 100644 tests/regression/val/id0_pretrained/pretrain_q.csv create mode 100644 tests/regression/val/id0_pretrained/progress.csv create mode 100644 tests/regression/val/id0_pretrained/script_name.txt create mode 100644 tests/regression/val/id0_pretrained/variant.json create mode 100644 tests/regression/val/id0_vqvae/debug.log create mode 100644 tests/regression/val/id0_vqvae/pixelcnn_progress.csv create mode 100644 tests/regression/val/id0_vqvae/progress.csv create mode 100644 tests/regression/val/id0_vqvae/script_name.txt create mode 100644 tests/regression/val/id0_vqvae/test_recon_0.png create mode 100644 tests/regression/val/id0_vqvae/train_recon_0.png create mode 100644 tests/regression/val/id0_vqvae/vae_progress.csv create mode 100644 tests/regression/val/id0_vqvae/variant.json create mode 100644 tests/regression/val/test_train_vqvae.py create mode 100644 tests/regression/val/test_val_pretrained.py diff --git a/.gitignore b/.gitignore index aa1c430c9..245126239 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,4 @@ rlkit/launchers/conf_private.py MANIFEST *.egg-info \.idea/ +.pytest_cache diff --git a/rlkit/core/rl_algorithm.py b/rlkit/core/rl_algorithm.py index 284ef46fd..fcc62362e 100644 --- a/rlkit/core/rl_algorithm.py +++ b/rlkit/core/rl_algorithm.py @@ -98,34 +98,34 @@ def _log_stats(self, epoch): """ logger.record_dict( self.expl_data_collector.get_diagnostics(), - prefix='exploration/' + prefix='expl/' ) expl_paths = self.expl_data_collector.get_epoch_paths() if hasattr(self.expl_env, 'get_diagnostics'): logger.record_dict( self.expl_env.get_diagnostics(expl_paths), - prefix='exploration/', + prefix='expl/', ) logger.record_dict( eval_util.get_generic_path_information(expl_paths), - prefix="exploration/", + prefix="expl/", ) """ Evaluation """ logger.record_dict( self.eval_data_collector.get_diagnostics(), - prefix='evaluation/', + prefix='eval/', ) eval_paths = self.eval_data_collector.get_epoch_paths() if hasattr(self.eval_env, 'get_diagnostics'): logger.record_dict( self.eval_env.get_diagnostics(eval_paths), - prefix='evaluation/', + prefix='eval/', ) logger.record_dict( eval_util.get_generic_path_information(eval_paths), - prefix="evaluation/", + prefix="eval/", ) """ diff --git a/rlkit/envs/proxy_env.py b/rlkit/envs/proxy_env.py index bb9a51a7a..032afd236 100644 --- a/rlkit/envs/proxy_env.py +++ b/rlkit/envs/proxy_env.py @@ -28,6 +28,9 @@ def terminate(self): if hasattr(self.wrapped_env, "terminate"): self.wrapped_env.terminate() + def seed(self, _seed): + return self.wrapped_env.seed(_seed) + def __getattr__(self, attr): if attr == '_wrapped_env': raise AttributeError() @@ -47,4 +50,4 @@ def __setstate__(self, state): self.__dict__.update(state) def __str__(self): - return '{}({})'.format(type(self).__name__, self.wrapped_env) \ No newline at end of file + return '{}({})'.format(type(self).__name__, self.wrapped_env) diff --git a/rlkit/envs/wrappers.py b/rlkit/envs/wrappers.py index ba83fe95c..3ba11b792 100644 --- a/rlkit/envs/wrappers.py +++ b/rlkit/envs/wrappers.py @@ -34,6 +34,9 @@ def terminate(self): if hasattr(self.wrapped_env, "terminate"): self.wrapped_env.terminate() + def seed(self, _seed): + return self.wrapped_env.seed(_seed) + def __getattr__(self, attr): if attr == '_wrapped_env': raise AttributeError() diff --git a/rlkit/launchers/launcher_util.py b/rlkit/launchers/launcher_util.py index ce330cb2f..775461b03 100644 --- a/rlkit/launchers/launcher_util.py +++ b/rlkit/launchers/launcher_util.py @@ -16,6 +16,7 @@ from rlkit.launchers import conf from rlkit.torch.pytorch_util import set_gpu_mode import rlkit.pythonplusplus as ppp +import torch GitInfo = namedtuple( 'GitInfo', @@ -353,6 +354,7 @@ def set_seed(seed): seed = int(seed) random.seed(seed) np.random.seed(seed) + torch.manual_seed(seed) def reset_execution_environment(): diff --git a/rlkit/testing/__init__.py b/rlkit/testing/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/rlkit/testing/csv_util.py b/rlkit/testing/csv_util.py new file mode 100644 index 000000000..08a159775 --- /dev/null +++ b/rlkit/testing/csv_util.py @@ -0,0 +1,26 @@ +import csv + +def get_exp(fname): + with open(fname) as csvfile: + reader = csv.DictReader(csvfile) + output = [] + for row in reader: + output.append(row) + return output + +def check_equal(reference, output, keys): + for i in range(len(reference)): + reference_row = reference[i] + output_row = output[i] + for key in keys: + assert key in reference_row, "line %d key %s not in reference" % (i, key) + assert key in output_row, "line %d key %s not in output" % (i, key) + assert reference_row[key] == output_row[key], "line %d key %s reference: %s, output: %s" % (i, key, reference_row[key], output_row[key]) + +def check_exactly_equal(reference, output, ): + for i in range(len(reference)): + reference_row = reference[i] + output_row = output[i] + for key in reference_row: + assert key in output_row, key + assert reference_row[key] == output_row[key], "%s reference: %s, output: %s" % (key, reference_row[key], output_row[key]) diff --git a/rlkit/testing/np_test_case.py b/rlkit/testing/np_test_case.py new file mode 100644 index 000000000..07bc4ae6a --- /dev/null +++ b/rlkit/testing/np_test_case.py @@ -0,0 +1,99 @@ +import unittest + +import numpy as np + +from rlkit.testing.testing_utils import are_np_arrays_equal, \ + are_np_array_iterables_equal + + +class NPTestCase(unittest.TestCase): + """ + Numpy test case, providing useful assert methods. + """ + def assertNpEqual(self, np_arr1, np_arr2, msg="Numpy arrays not equal."): + self.assertTrue(are_np_arrays_equal(np_arr1, np_arr2), msg) + + def assertNpAlmostEqual( + self, + np_arr1, + np_arr2, + msg="Numpy arrays not equal.", + threshold=1e-5, + ): + self.assertTrue( + are_np_arrays_equal(np_arr1, np_arr2, threshold=threshold), + msg + ) + + def assertNpNotEqual(self, np_arr1, np_arr2, msg="Numpy arrays equal"): + self.assertFalse(are_np_arrays_equal(np_arr1, np_arr2), msg) + + def assertNpArraysEqual( + self, + np_arrays1, + np_arrays2, + msg=None, + ): + msg = msg or "Numpy arrays {} and {} are not equal".format( + np_arrays1, + np_arrays2, + ) + self.assertTrue( + are_np_array_iterables_equal( + np_arrays1, + np_arrays2, + ), + msg + ) + + # TODO(vpong): see why such a high threshold is needed + def assertNpArraysAlmostEqual( + self, + np_arrays1, + np_arrays2, + msg="Numpy array lists are not almost equal.", + threshold=1e-4, + ): + self.assertTrue( + are_np_array_iterables_equal( + np_arrays1, + np_arrays2, + threshold=threshold, + ), + msg + ) + + def assertNpArraysNotEqual( + self, + np_arrays1, + np_arrays2, + msg="Numpy array lists are equal." + ): + self.assertFalse(are_np_array_iterables_equal(np_arrays1, np_arrays2), + msg) + + def assertNpArraysNotAlmostEqual( + self, + np_arrays1, + np_arrays2, + msg="Numpy array lists are equal.", + threshold=1e-4, + ): + self.assertFalse( + are_np_array_iterables_equal( + np_arrays1, + np_arrays2, + threshold=threshold, + ), + msg + ) + + def assertNpArrayConstant( + self, + np_array: np.ndarray, + constant + ): + self.assertTrue( + (np_array == constant).all(), + msg="Not all values equal {0}".format(constant) + ) \ No newline at end of file diff --git a/rlkit/testing/stub_classes.py b/rlkit/testing/stub_classes.py new file mode 100644 index 000000000..4ea7d8c14 --- /dev/null +++ b/rlkit/testing/stub_classes.py @@ -0,0 +1,103 @@ +import numpy as np +from gym.spaces import Box, Dict + +from rlkit.exploration_strategies.base import RawExplorationStrategy + +class StubEnv(object): + def __init__(self, obs_dim=1, action_dim=1, **kwargs): + self.obs_dim = obs_dim + obs_low = np.ones(obs_dim) * -1 + obs_high = np.ones(obs_dim) + self._observation_space = Box(obs_low, obs_high) + + self.action_dim = action_dim + action_low = np.ones(action_dim) * -1 + action_high = np.ones(action_dim) + self._action_space = Box(action_low, action_high) + + print("stub env unused kwargs", kwargs) + + def reset(self): + return np.zeros(self.obs_dim) + + def step(self, action): + return np.zeros(self.obs_dim), 0, 0, {} + + @property + def action_space(self): + return self._action_space + + @property + def horizon(self): + return 99999 + + @property + def observation_space(self): + return self._observation_space + +class StubMultiEnv(object): + def __init__(self, obs_dims=None, action_dim=1, **kwargs): + self.obs_dims = obs_dims + + spaces = [] + for name in self.obs_dims: + obs_dim = self.obs_dims[name] + obs_low = np.ones(obs_dim) * -1 + obs_high = np.ones(obs_dim) + spaces.append((name, Box(obs_low, obs_high))) + self._observation_space = Dict(spaces) + + self.action_dim = action_dim + action_low = np.ones(action_dim) * -1 + action_high = np.ones(action_dim) + self._action_space = Box(action_low, action_high) + + print("stub env unused kwargs", kwargs) + + def reset(self): + return self.get_obs() + + def step(self, action): + return self.get_obs(), 0, 0, {} + + def get_obs(self): + obs = dict() + for name in self.obs_dims: + obs_dim = self.obs_dims[name] + obs[name] = np.zeros(obs_dim) + return obs + + @property + def action_space(self): + return self._action_space + + @property + def horizon(self): + return 99999 + + @property + def observation_space(self): + return self._observation_space + + +class StubPolicy(object): + def __init__(self, action): + self._action = action + + def get_action(self, *arg, **kwargs): + return self._action, {} + + +class AddEs(RawExplorationStrategy): + """ + return action + constant + """ + def __init__(self, number): + self._number = number + + def get_action(self, t, observation, policy, **kwargs): + action, _ = policy.get_action(observation) + return self.get_action_from_raw_action(action) + + def get_action_from_raw_action(self, action, **kwargs): + return self._number + action diff --git a/rlkit/testing/testing_utils.py b/rlkit/testing/testing_utils.py new file mode 100644 index 000000000..b66e9c32c --- /dev/null +++ b/rlkit/testing/testing_utils.py @@ -0,0 +1,52 @@ +import math +import numpy as np +from numbers import Number + + +def is_binomial_trial_likely(n, p, num_success, num_std=3): + """ + Returns whether or not seeing `num_sucesss` successes is likely. + :param n: Number of trials. + :param p: Probability of success. + :param num_success: Number of successes + :param num_std: Number of standard deviations the results must be within. + :return: + """ + mean = n * p + std = math.sqrt(n * p * (1 - p)) + margin = num_std * std + return mean - margin < num_success < mean + margin + + +def are_np_array_iterables_equal(np_list1, np_list2, threshold=1e-5): + # import ipdb; ipdb.set_trace() + # if isinstance(np_list1.shape ==, Number) and isinstance(np_itr2, Number): + # return are_np_arrays_equal(np_itr1, np_itr2) + if np_list1.shape == () and np_list2.shape == (): + return are_np_arrays_equal(np_list1, np_list2) + # in case generators were passed in + # np_list1 = list(np_itr1) + # np_list2 = list(np_itr2) + return ( + len(np_list1) == len(np_list2) and + all(are_np_arrays_equal(arr1, arr2, threshold=threshold) + for arr1, arr2 in zip(np_list1, np_list2)) + ) + + +def are_np_arrays_equal(arr1, arr2, threshold=1e-5): + if arr1.shape != arr2.shape: + return False + return (np.abs(arr1 - arr2) <= threshold).all() + + +def is_list_subset(list1, list2): + for a in list1: + if a not in list2: + return False + return True + + +def are_dict_lists_equal(list1, list2): + return is_list_subset(list1, list2) and is_list_subset(list2, list1) + diff --git a/rlkit/testing/tf_test_case.py b/rlkit/testing/tf_test_case.py new file mode 100644 index 000000000..a128b913a --- /dev/null +++ b/rlkit/testing/tf_test_case.py @@ -0,0 +1,40 @@ +import numpy as np +import tensorflow as tf + +from rlkit.testing.np_test_case import NPTestCase + + +class TFTestCase(NPTestCase): + """ + Tensorflow test case, providing useful assert methods and clean default + session. + """ + def setUp(self): + tf.reset_default_graph() + self.sess = tf.get_default_session() or tf.Session() + self.sess_context = self.sess.as_default() + self.sess_context.__enter__() + + def tearDown(self): + self.sess_context.__exit__(None, None, None) + self.sess.close() + + def assertParamsEqual(self, network1, network2): + self.assertNpArraysEqual( + network1.get_param_values(), + network2.get_param_values(), + msg="Parameters are not equal.", + ) + + def assertParamsNotEqual(self, network1, network2): + self.assertNpArraysNotEqual( + network1.get_param_values(), + network2.get_param_values(), + msg="Parameters are equal.", + ) + + def randomize_param_values(self, network): + for v in network.get_params(): + self.sess.run( + v.assign(np.random.rand(*v.get_shape())) + ) diff --git a/tests/regression/__init__.py b/tests/regression/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/regression/awac/__init__.py b/tests/regression/awac/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/regression/awac/d4rl/__init__.py b/tests/regression/awac/d4rl/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/regression/awac/d4rl/id0/debug.log b/tests/regression/awac/d4rl/id0/debug.log new file mode 100644 index 000000000..dfb7ae842 --- /dev/null +++ b/tests/regression/awac/d4rl/id0/debug.log @@ -0,0 +1,1779 @@ +[id0] ------------------------------ --------------- +[id0] trainer/QF1 Loss 36.8715 +[id0] trainer/QF2 Loss 36.8755 +[id0] trainer/Policy Loss 9.28352 +[id0] trainer/Q1 Predictions Mean -0.000248199 +[id0] trainer/Q1 Predictions Std 0.00343049 +[id0] trainer/Q1 Predictions Max 0.0135826 +[id0] trainer/Q1 Predictions Min -0.00886565 +[id0] trainer/Q2 Predictions Mean 0.000417902 +[id0] trainer/Q2 Predictions Std 0.00379635 +[id0] trainer/Q2 Predictions Max 0.0113527 +[id0] trainer/Q2 Predictions Min 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length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean 0.00469175 +[id0] expl/Rewards Std 0.119686 +[id0] expl/Rewards Max 0.284136 +[id0] expl/Rewards Min -0.282654 +[id0] expl/Returns Mean 0.469175 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max 0.469175 +[id0] expl/Returns Min 0.469175 +[id0] expl/Actions Mean 0.00324672 +[id0] expl/Actions Std 0.0820437 +[id0] expl/Actions Max 0.223926 +[id0] expl/Actions Min -0.21157 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns 0.469175 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.321473 +[id0] time/evaluation sampling (s) 0.0823779 +[id0] time/exploration sampling (s) 0.0821865 +[id0] time/global_time (s) 7.41728 +[id0] time/replay buffer data storing (s) 0.000601768 +[id0] time/saving (s) 1.5974e-05 +[id0] time/training (s) 0.155414 +[id0] epoch 4 +[id0] ----------------------------------- --------------- +[id0] ------------------------------ --------------- +[id0] trainer/QF1 Loss 36.8715 +[id0] trainer/QF2 Loss 36.8755 +[id0] trainer/Policy Loss 9.28352 +[id0] trainer/Q1 Predictions Mean -0.000248199 +[id0] trainer/Q1 Predictions Std 0.00343049 +[id0] trainer/Q1 Predictions Max 0.0135826 +[id0] trainer/Q1 Predictions Min -0.00886565 +[id0] trainer/Q2 Predictions Mean 0.000417902 +[id0] trainer/Q2 Predictions Std 0.00379635 +[id0] trainer/Q2 Predictions Max 0.0113527 +[id0] trainer/Q2 Predictions Min -0.0108211 +[id0] trainer/Q Targets Mean 3.5774 +[id0] trainer/Q Targets Std 4.90573 +[id0] trainer/Q Targets Max 12.3087 +[id0] trainer/Q Targets Min -1.71089 +[id0] trainer/Log Pis Mean 9.2814 +[id0] trainer/Log Pis Std 1.9335 +[id0] trainer/Log Pis Max 12.1028 +[id0] trainer/Log Pis Min 1.67644 +[id0] trainer/rewards Mean 3.58356 +[id0] trainer/rewards Std 4.9069 +[id0] trainer/rewards Max 12.316 +[id0] trainer/rewards Min -1.70161 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89000 +[id0] trainer/policy/mean Mean -0.000101677 +[id0] trainer/policy/mean Std 0.000327078 +[id0] trainer/policy/mean Max 0.000704126 +[id0] trainer/policy/mean Min -0.0014804 +[id0] trainer/policy/std Mean 0.0497871 +[id0] trainer/policy/std Std 3.72529e-09 +[id0] trainer/policy/std Max 0.0497871 +[id0] trainer/policy/std Min 0.0497871 +[id0] trainer/Advantage Weights Mean 0.0078125 +[id0] trainer/Advantage Weights Std 1.97209e-06 +[id0] trainer/Advantage Weights Max 0.00781739 +[id0] trainer/Advantage Weights Min 0.00780739 +[id0] trainer/Advantage Score Mean 5.75556e-05 +[id0] trainer/Advantage Score Std 0.000504857 +[id0] trainer/Advantage Score Max 0.00130917 +[id0] trainer/Advantage Score Min -0.00124966 +[id0] trainer/batch 0 +[id0] trainer/epoch_time 0.0225158 +[id0] ------------------------------ --------------- +[id0] ----------------------------------- --------------- +[id0] trainer/num train calls 10 +[id0] trainer/QF1 Loss 92.1218 +[id0] trainer/QF2 Loss 91.9529 +[id0] trainer/Policy Loss 8.70062 +[id0] trainer/Q1 Predictions Mean 0.243832 +[id0] trainer/Q1 Predictions Std 0.04779 +[id0] trainer/Q1 Predictions Max 0.299389 +[id0] trainer/Q1 Predictions Min 0.179337 +[id0] trainer/Q2 Predictions Mean 0.242781 +[id0] trainer/Q2 Predictions Std 0.0598606 +[id0] trainer/Q2 Predictions Max 0.301608 +[id0] trainer/Q2 Predictions Min 0.137358 +[id0] trainer/Q Targets Mean 8.68214 +[id0] trainer/Q Targets Std 4.60552 +[id0] trainer/Q Targets Max 11.1447 +[id0] trainer/Q Targets Min -0.527146 +[id0] trainer/Log Pis Mean 8.93413 +[id0] trainer/Log Pis Std 1.90035 +[id0] trainer/Log Pis Max 10.6362 +[id0] trainer/Log Pis Min 5.43167 +[id0] trainer/rewards Mean 8.68726 +[id0] trainer/rewards Std 4.60638 +[id0] trainer/rewards Max 11.1469 +[id0] trainer/rewards Min -0.523798 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89110 +[id0] trainer/policy/mean Mean 0.0501946 +[id0] trainer/policy/mean Std 0.0769959 +[id0] trainer/policy/mean Max 0.164765 +[id0] trainer/policy/mean Min -0.141686 +[id0] trainer/policy/std Mean 0.0500109 +[id0] trainer/policy/std Std 1.7139e-07 +[id0] trainer/policy/std Max 0.0500112 +[id0] trainer/policy/std Min 0.0500107 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.000215922 +[id0] trainer/Advantage Weights Max 0.200264 +[id0] trainer/Advantage Weights Min 0.199614 +[id0] trainer/Advantage Score Mean 0.00193172 +[id0] trainer/Advantage Score Std 0.00216014 +[id0] trainer/Advantage Score Max 0.00457418 +[id0] trainer/Advantage Score Min -0.00193453 +[id0] expl/num steps total 110 +[id0] expl/num paths total 2 +[id0] expl/path length Mean 100 +[id0] expl/path length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean -0.00721015 +[id0] expl/Rewards Std 0.0712339 +[id0] expl/Rewards Max 0.121941 +[id0] expl/Rewards Min -0.157629 +[id0] expl/Returns Mean -0.721015 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max -0.721015 +[id0] expl/Returns Min -0.721015 +[id0] expl/Actions Mean 0.003563 +[id0] expl/Actions Std 0.0494889 +[id0] expl/Actions Max 0.156353 +[id0] expl/Actions Min -0.174789 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns -0.721015 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.362828 +[id0] time/evaluation sampling (s) 0.0890572 +[id0] time/exploration sampling (s) 0.0914454 +[id0] time/global_time (s) 6.12744 +[id0] time/replay buffer data storing (s) 0.000605106 +[id0] time/saving (s) 0.0110779 +[id0] time/training (s) 0.15896 +[id0] epoch 0 +[id0] ----------------------------------- --------------- +[id0] ----------------------------------- --------------- +[id0] trainer/num train calls 20 +[id0] trainer/QF1 Loss 57.5256 +[id0] trainer/QF2 Loss 57.7265 +[id0] trainer/Policy Loss 9.03843 +[id0] trainer/Q1 Predictions Mean 0.610775 +[id0] trainer/Q1 Predictions Std 0.183916 +[id0] trainer/Q1 Predictions Max 0.854407 +[id0] trainer/Q1 Predictions Min 0.396609 +[id0] trainer/Q2 Predictions Mean 0.610474 +[id0] trainer/Q2 Predictions Std 0.159908 +[id0] trainer/Q2 Predictions Max 0.800834 +[id0] trainer/Q2 Predictions Min 0.367608 +[id0] trainer/Q Targets Mean 6.21657 +[id0] trainer/Q Targets Std 5.26535 +[id0] trainer/Q Targets Max 11.7897 +[id0] trainer/Q Targets Min -0.836062 +[id0] trainer/Log Pis Mean 9.60988 +[id0] trainer/Log Pis Std 0.939456 +[id0] trainer/Log Pis Max 11.2394 +[id0] trainer/Log Pis Min 8.45266 +[id0] trainer/rewards Mean 6.21509 +[id0] trainer/rewards Std 5.26596 +[id0] trainer/rewards Max 11.7865 +[id0] trainer/rewards Min -0.839532 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89210 +[id0] trainer/policy/mean Mean 0.1687 +[id0] trainer/policy/mean Std 0.298864 +[id0] trainer/policy/mean Max 0.587666 +[id0] trainer/policy/mean Min -0.548678 +[id0] trainer/policy/std Mean 0.0502259 +[id0] trainer/policy/std Std 2.68281e-06 +[id0] trainer/policy/std Max 0.0502315 +[id0] trainer/policy/std Min 0.0502227 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.000468149 +[id0] trainer/Advantage Weights Max 0.200907 +[id0] trainer/Advantage Weights Min 0.199639 +[id0] trainer/Advantage Score Mean 0.00256994 +[id0] trainer/Advantage Score Std 0.00467449 +[id0] trainer/Advantage Score Max 0.0116208 +[id0] trainer/Advantage Score Min -0.00104058 +[id0] expl/num steps total 210 +[id0] expl/num paths total 3 +[id0] expl/path length Mean 100 +[id0] expl/path length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean -0.0164893 +[id0] expl/Rewards Std 0.0869197 +[id0] expl/Rewards Max 0.176334 +[id0] expl/Rewards Min -0.305826 +[id0] expl/Returns Mean -1.64893 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max -1.64893 +[id0] expl/Returns Min -1.64893 +[id0] expl/Actions Mean 0.0180473 +[id0] expl/Actions Std 0.0550493 +[id0] expl/Actions Max 0.222433 +[id0] expl/Actions Min -0.128606 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns -1.64893 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.340481 +[id0] time/evaluation sampling (s) 0.0897565 +[id0] time/exploration sampling (s) 0.0893493 +[id0] time/global_time (s) 6.47115 +[id0] time/replay buffer data storing (s) 0.000620842 +[id0] time/saving (s) 1.74046e-05 +[id0] time/training (s) 0.15979 +[id0] epoch 1 +[id0] ----------------------------------- --------------- +[id0] ----------------------------------- --------------- +[id0] trainer/num train calls 30 +[id0] trainer/QF1 Loss 27.1734 +[id0] trainer/QF2 Loss 27.0553 +[id0] trainer/Policy Loss 9.20581 +[id0] trainer/Q1 Predictions Mean 1.22454 +[id0] trainer/Q1 Predictions Std 0.446581 +[id0] trainer/Q1 Predictions Max 1.82785 +[id0] trainer/Q1 Predictions Min 0.645031 +[id0] trainer/Q2 Predictions Mean 1.17344 +[id0] trainer/Q2 Predictions Std 0.463408 +[id0] trainer/Q2 Predictions Max 1.8045 +[id0] trainer/Q2 Predictions Min 0.545251 +[id0] trainer/Q Targets Mean 4.36755 +[id0] trainer/Q Targets Std 4.29701 +[id0] trainer/Q Targets Max 9.30498 +[id0] trainer/Q Targets Min -0.915291 +[id0] trainer/Log Pis Mean 10.3622 +[id0] trainer/Log Pis Std 0.543725 +[id0] trainer/Log Pis Max 11.2877 +[id0] trainer/Log Pis Min 9.68874 +[id0] trainer/rewards Mean 4.35784 +[id0] trainer/rewards Std 4.29613 +[id0] trainer/rewards Max 9.2929 +[id0] trainer/rewards Min -0.920588 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89310 +[id0] trainer/policy/mean Mean 0.0729202 +[id0] trainer/policy/mean Std 0.519411 +[id0] trainer/policy/mean Max 0.812271 +[id0] trainer/policy/mean Min -0.82246 +[id0] trainer/policy/std Mean 0.0504347 +[id0] trainer/policy/std Std 4.48409e-06 +[id0] trainer/policy/std Max 0.0504402 +[id0] trainer/policy/std Min 0.0504286 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.000429373 +[id0] trainer/Advantage Weights Max 0.20049 +[id0] trainer/Advantage Weights Min 0.199444 +[id0] trainer/Advantage Score Mean 0.00394787 +[id0] trainer/Advantage Score Std 0.0042947 +[id0] trainer/Advantage Score Max 0.00884658 +[id0] trainer/Advantage Score Min -0.00161982 +[id0] expl/num steps total 310 +[id0] expl/num paths total 4 +[id0] expl/path length Mean 100 +[id0] expl/path length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean -0.0141588 +[id0] expl/Rewards Std 0.133482 +[id0] expl/Rewards Max 0.251953 +[id0] expl/Rewards Min -0.533384 +[id0] expl/Returns Mean -1.41588 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max -1.41588 +[id0] expl/Returns Min -1.41588 +[id0] expl/Actions Mean 0.0280376 +[id0] expl/Actions Std 0.0677875 +[id0] expl/Actions Max 0.25402 +[id0] expl/Actions Min -0.163569 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns -1.41588 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.332668 +[id0] time/evaluation sampling (s) 0.0876844 +[id0] time/exploration sampling (s) 0.0913177 +[id0] time/global_time (s) 6.80666 +[id0] time/replay buffer data storing (s) 0.000616312 +[id0] time/saving (s) 1.69277e-05 +[id0] time/training (s) 0.152097 +[id0] epoch 2 +[id0] ----------------------------------- --------------- +[id0] ----------------------------------- --------------- +[id0] trainer/num train calls 40 +[id0] trainer/QF1 Loss 27.5874 +[id0] trainer/QF2 Loss 24.4089 +[id0] trainer/Policy Loss 7.8244 +[id0] trainer/Q1 Predictions Mean 1.43631 +[id0] trainer/Q1 Predictions Std 0.695864 +[id0] trainer/Q1 Predictions Max 2.35928 +[id0] trainer/Q1 Predictions Min 0.501491 +[id0] trainer/Q2 Predictions Mean 1.72835 +[id0] trainer/Q2 Predictions Std 0.874067 +[id0] trainer/Q2 Predictions Max 2.90332 +[id0] trainer/Q2 Predictions Min 0.604359 +[id0] trainer/Q Targets Mean 4.25011 +[id0] trainer/Q Targets Std 5.1101 +[id0] trainer/Q Targets Max 11.5322 +[id0] trainer/Q Targets Min -1.23925 +[id0] trainer/Log Pis Mean 9.25355 +[id0] trainer/Log Pis Std 0.578658 +[id0] trainer/Log Pis Max 10.16 +[id0] trainer/Log Pis Min 8.5383 +[id0] trainer/rewards Mean 4.22912 +[id0] trainer/rewards Std 5.11 +[id0] trainer/rewards Max 11.5097 +[id0] trainer/rewards Min -1.25702 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89410 +[id0] trainer/policy/mean Mean 0.236795 +[id0] trainer/policy/mean Std 0.2788 +[id0] trainer/policy/mean Max 0.742046 +[id0] trainer/policy/mean Min -0.260477 +[id0] trainer/policy/std Mean 0.0506312 +[id0] trainer/policy/std Std 1.46128e-05 +[id0] trainer/policy/std Max 0.0506556 +[id0] trainer/policy/std Min 0.0506164 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.000854139 +[id0] trainer/Advantage Weights Max 0.201628 +[id0] trainer/Advantage Weights Min 0.199138 +[id0] trainer/Advantage Score Mean 0.00716419 +[id0] trainer/Advantage Score Std 0.00852061 +[id0] trainer/Advantage Score Max 0.0233918 +[id0] trainer/Advantage Score Min -0.00145853 +[id0] expl/num steps total 410 +[id0] expl/num paths total 5 +[id0] expl/path length Mean 100 +[id0] expl/path length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean -0.00741024 +[id0] expl/Rewards Std 0.0916669 +[id0] expl/Rewards Max 0.232838 +[id0] expl/Rewards Min -0.262547 +[id0] expl/Returns Mean -0.741024 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max -0.741024 +[id0] expl/Returns Min -0.741024 +[id0] expl/Actions Mean 0.0152881 +[id0] expl/Actions Std 0.0606559 +[id0] expl/Actions Max 0.201034 +[id0] expl/Actions Min -0.158058 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns -0.741024 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.342567 +[id0] time/evaluation sampling (s) 0.0891957 +[id0] time/exploration sampling (s) 0.0910256 +[id0] time/global_time (s) 7.15239 +[id0] time/replay buffer data storing (s) 0.000618458 +[id0] time/saving (s) 1.71661e-05 +[id0] time/training (s) 0.160674 +[id0] epoch 3 +[id0] ----------------------------------- --------------- +[id0] ----------------------------------- --------------- +[id0] trainer/num train calls 50 +[id0] trainer/QF1 Loss 12.0632 +[id0] trainer/QF2 Loss 12.4672 +[id0] trainer/Policy Loss 5.22708 +[id0] trainer/Q1 Predictions Mean 3.13504 +[id0] trainer/Q1 Predictions Std 1.28296 +[id0] trainer/Q1 Predictions Max 4.55434 +[id0] trainer/Q1 Predictions Min 1.47195 +[id0] trainer/Q2 Predictions Mean 3.27497 +[id0] trainer/Q2 Predictions Std 1.37992 +[id0] trainer/Q2 Predictions Max 4.7188 +[id0] trainer/Q2 Predictions Min 1.39781 +[id0] trainer/Q Targets Mean 4.40037 +[id0] trainer/Q Targets Std 4.29655 +[id0] trainer/Q Targets Max 10.2577 +[id0] trainer/Q Targets Min -0.60194 +[id0] trainer/Log Pis Mean 8.32517 +[id0] trainer/Log Pis Std 1.02499 +[id0] trainer/Log Pis Max 9.8133 +[id0] trainer/Log Pis Min 7.10904 +[id0] trainer/rewards Mean 4.36342 +[id0] trainer/rewards Std 4.28988 +[id0] trainer/rewards Max 10.2231 +[id0] trainer/rewards Min -0.621302 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89510 +[id0] trainer/policy/mean Mean 0.0279084 +[id0] trainer/policy/mean Std 0.513607 +[id0] trainer/policy/mean Max 0.844352 +[id0] trainer/policy/mean Min -0.899527 +[id0] trainer/policy/std Mean 0.0508098 +[id0] trainer/policy/std Std 2.20989e-05 +[id0] trainer/policy/std Max 0.05085 +[id0] trainer/policy/std Min 0.0507816 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.00161367 +[id0] trainer/Advantage Weights Max 0.202964 +[id0] trainer/Advantage Weights Min 0.198459 +[id0] trainer/Advantage Score Mean 0.0103492 +[id0] trainer/Advantage Score Std 0.0160741 +[id0] trainer/Advantage Score Max 0.0398329 +[id0] trainer/Advantage Score Min -0.00505543 +[id0] expl/num steps total 510 +[id0] expl/num paths total 6 +[id0] expl/path length Mean 100 +[id0] expl/path length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean -0.00873481 +[id0] expl/Rewards Std 0.125206 +[id0] expl/Rewards Max 0.283799 +[id0] expl/Rewards Min -0.292935 +[id0] expl/Returns Mean -0.873481 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max -0.873481 +[id0] expl/Returns Min -0.873481 +[id0] expl/Actions Mean 0.0032692 +[id0] expl/Actions Std 0.0847575 +[id0] expl/Actions Max 0.334187 +[id0] expl/Actions Min -0.348001 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns -0.873481 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.339243 +[id0] time/evaluation sampling (s) 0.0907469 +[id0] time/exploration sampling (s) 0.0875156 +[id0] time/global_time (s) 7.49486 +[id0] time/replay buffer data storing (s) 0.000614643 +[id0] time/saving (s) 1.81198e-05 +[id0] time/training (s) 0.159385 +[id0] epoch 4 +[id0] ----------------------------------- --------------- +[id0] ------------------------------ --------------- +[id0] trainer/QF1 Loss 36.8715 +[id0] trainer/QF2 Loss 36.8755 +[id0] trainer/Policy Loss 9.28352 +[id0] trainer/Q1 Predictions Mean -0.000248199 +[id0] trainer/Q1 Predictions Std 0.00343049 +[id0] trainer/Q1 Predictions Max 0.0135826 +[id0] trainer/Q1 Predictions Min -0.00886565 +[id0] trainer/Q2 Predictions Mean 0.000417902 +[id0] trainer/Q2 Predictions Std 0.00379635 +[id0] trainer/Q2 Predictions Max 0.0113527 +[id0] trainer/Q2 Predictions Min -0.0108211 +[id0] trainer/Q Targets Mean 3.5774 +[id0] trainer/Q Targets Std 4.90573 +[id0] trainer/Q Targets Max 12.3087 +[id0] trainer/Q Targets Min -1.71089 +[id0] trainer/Log Pis Mean 9.2814 +[id0] trainer/Log Pis Std 1.9335 +[id0] trainer/Log Pis Max 12.1028 +[id0] trainer/Log Pis Min 1.67644 +[id0] trainer/rewards Mean 3.58356 +[id0] trainer/rewards Std 4.9069 +[id0] trainer/rewards Max 12.316 +[id0] trainer/rewards Min -1.70161 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89000 +[id0] trainer/policy/mean Mean -0.000101677 +[id0] trainer/policy/mean Std 0.000327078 +[id0] trainer/policy/mean Max 0.000704126 +[id0] trainer/policy/mean Min -0.0014804 +[id0] trainer/policy/std Mean 0.0497871 +[id0] trainer/policy/std Std 3.72529e-09 +[id0] trainer/policy/std Max 0.0497871 +[id0] trainer/policy/std Min 0.0497871 +[id0] trainer/Advantage Weights Mean 0.0078125 +[id0] trainer/Advantage Weights Std 1.97209e-06 +[id0] trainer/Advantage Weights Max 0.00781739 +[id0] trainer/Advantage Weights Min 0.00780739 +[id0] trainer/Advantage Score Mean 5.75556e-05 +[id0] trainer/Advantage Score Std 0.000504857 +[id0] trainer/Advantage Score Max 0.00130917 +[id0] trainer/Advantage Score Min -0.00124966 +[id0] trainer/batch 0 +[id0] trainer/epoch_time 0.0222483 +[id0] ------------------------------ --------------- +[id0] ----------------------------------- --------------- +[id0] trainer/num train calls 10 +[id0] trainer/QF1 Loss 92.1218 +[id0] trainer/QF2 Loss 91.9529 +[id0] trainer/Policy Loss 8.70062 +[id0] trainer/Q1 Predictions Mean 0.243832 +[id0] trainer/Q1 Predictions Std 0.04779 +[id0] trainer/Q1 Predictions Max 0.299389 +[id0] trainer/Q1 Predictions Min 0.179337 +[id0] trainer/Q2 Predictions Mean 0.242781 +[id0] trainer/Q2 Predictions Std 0.0598606 +[id0] trainer/Q2 Predictions Max 0.301608 +[id0] trainer/Q2 Predictions Min 0.137358 +[id0] trainer/Q Targets Mean 8.68214 +[id0] trainer/Q Targets Std 4.60552 +[id0] trainer/Q Targets Max 11.1447 +[id0] trainer/Q Targets Min -0.527146 +[id0] trainer/Log Pis Mean 8.93413 +[id0] trainer/Log Pis Std 1.90035 +[id0] trainer/Log Pis Max 10.6362 +[id0] trainer/Log Pis Min 5.43167 +[id0] trainer/rewards Mean 8.68726 +[id0] trainer/rewards Std 4.60638 +[id0] trainer/rewards Max 11.1469 +[id0] trainer/rewards Min -0.523798 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89110 +[id0] trainer/policy/mean Mean 0.0501946 +[id0] trainer/policy/mean Std 0.0769959 +[id0] trainer/policy/mean Max 0.164765 +[id0] trainer/policy/mean Min -0.141686 +[id0] trainer/policy/std Mean 0.0500109 +[id0] trainer/policy/std Std 1.7139e-07 +[id0] trainer/policy/std Max 0.0500112 +[id0] trainer/policy/std Min 0.0500107 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.000215922 +[id0] trainer/Advantage Weights Max 0.200264 +[id0] trainer/Advantage Weights Min 0.199614 +[id0] trainer/Advantage Score Mean 0.00193172 +[id0] trainer/Advantage Score Std 0.00216014 +[id0] trainer/Advantage Score Max 0.00457418 +[id0] trainer/Advantage Score Min -0.00193453 +[id0] expl/num steps total 110 +[id0] expl/num paths total 2 +[id0] expl/path length Mean 100 +[id0] expl/path length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean 0.00543224 +[id0] expl/Rewards Std 0.0953588 +[id0] expl/Rewards Max 0.385852 +[id0] expl/Rewards Min -0.220959 +[id0] expl/Returns Mean 0.543224 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max 0.543224 +[id0] expl/Returns Min 0.543224 +[id0] expl/Actions Mean 0.00368127 +[id0] expl/Actions Std 0.049537 +[id0] expl/Actions Max 0.156353 +[id0] expl/Actions Min -0.180871 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns 0.543224 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.378024 +[id0] time/evaluation sampling (s) 0.0921521 +[id0] time/exploration sampling (s) 0.0855072 +[id0] time/global_time (s) 6.1888 +[id0] time/replay buffer data storing (s) 0.00057745 +[id0] time/saving (s) 0.0299449 +[id0] time/training (s) 0.157263 +[id0] epoch 0 +[id0] ----------------------------------- --------------- +[id0] ----------------------------------- --------------- +[id0] trainer/num train calls 20 +[id0] trainer/QF1 Loss 57.5256 +[id0] trainer/QF2 Loss 57.7265 +[id0] trainer/Policy Loss 9.03843 +[id0] trainer/Q1 Predictions Mean 0.610775 +[id0] trainer/Q1 Predictions Std 0.183916 +[id0] trainer/Q1 Predictions Max 0.854407 +[id0] trainer/Q1 Predictions Min 0.396609 +[id0] trainer/Q2 Predictions Mean 0.610474 +[id0] trainer/Q2 Predictions Std 0.159908 +[id0] trainer/Q2 Predictions Max 0.800834 +[id0] trainer/Q2 Predictions Min 0.367608 +[id0] trainer/Q Targets Mean 6.21657 +[id0] trainer/Q Targets Std 5.26535 +[id0] trainer/Q Targets Max 11.7897 +[id0] trainer/Q Targets Min -0.836062 +[id0] trainer/Log Pis Mean 9.60988 +[id0] trainer/Log Pis Std 0.939456 +[id0] trainer/Log Pis Max 11.2394 +[id0] trainer/Log Pis Min 8.45266 +[id0] trainer/rewards Mean 6.21509 +[id0] trainer/rewards Std 5.26596 +[id0] trainer/rewards Max 11.7865 +[id0] trainer/rewards Min -0.839532 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89210 +[id0] trainer/policy/mean Mean 0.1687 +[id0] trainer/policy/mean Std 0.298864 +[id0] trainer/policy/mean Max 0.587666 +[id0] trainer/policy/mean Min -0.548678 +[id0] trainer/policy/std Mean 0.0502259 +[id0] trainer/policy/std Std 2.68281e-06 +[id0] trainer/policy/std Max 0.0502315 +[id0] trainer/policy/std Min 0.0502227 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.000468149 +[id0] trainer/Advantage Weights Max 0.200907 +[id0] trainer/Advantage Weights Min 0.199639 +[id0] trainer/Advantage Score Mean 0.00256994 +[id0] trainer/Advantage Score Std 0.00467449 +[id0] trainer/Advantage Score Max 0.0116208 +[id0] trainer/Advantage Score Min -0.00104058 +[id0] expl/num steps total 210 +[id0] expl/num paths total 3 +[id0] expl/path length Mean 100 +[id0] expl/path length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean -0.00628498 +[id0] expl/Rewards Std 0.0770803 +[id0] expl/Rewards Max 0.176332 +[id0] expl/Rewards Min -0.169923 +[id0] expl/Returns Mean -0.628498 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max -0.628498 +[id0] expl/Returns Min -0.628498 +[id0] expl/Actions Mean 0.017509 +[id0] expl/Actions Std 0.0540245 +[id0] expl/Actions Max 0.170002 +[id0] expl/Actions Min -0.128605 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns -0.628498 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.337391 +[id0] time/evaluation sampling (s) 0.0868249 +[id0] time/exploration sampling (s) 0.0873156 +[id0] time/global_time (s) 6.52971 +[id0] time/replay buffer data storing (s) 0.00095439 +[id0] time/saving (s) 1.62125e-05 +[id0] time/training (s) 0.161433 +[id0] epoch 1 +[id0] ----------------------------------- --------------- +[id0] ----------------------------------- --------------- +[id0] trainer/num train calls 30 +[id0] trainer/QF1 Loss 27.1734 +[id0] trainer/QF2 Loss 27.0553 +[id0] trainer/Policy Loss 9.20581 +[id0] trainer/Q1 Predictions Mean 1.22454 +[id0] trainer/Q1 Predictions Std 0.446581 +[id0] trainer/Q1 Predictions Max 1.82785 +[id0] trainer/Q1 Predictions Min 0.645031 +[id0] trainer/Q2 Predictions Mean 1.17344 +[id0] trainer/Q2 Predictions Std 0.463408 +[id0] trainer/Q2 Predictions Max 1.8045 +[id0] trainer/Q2 Predictions Min 0.545251 +[id0] trainer/Q Targets Mean 4.36755 +[id0] trainer/Q Targets Std 4.29701 +[id0] trainer/Q Targets Max 9.30498 +[id0] trainer/Q Targets Min -0.915291 +[id0] trainer/Log Pis Mean 10.3622 +[id0] trainer/Log Pis Std 0.543725 +[id0] trainer/Log Pis Max 11.2877 +[id0] trainer/Log Pis Min 9.68874 +[id0] trainer/rewards Mean 4.35784 +[id0] trainer/rewards Std 4.29613 +[id0] trainer/rewards Max 9.2929 +[id0] trainer/rewards Min -0.920588 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89310 +[id0] trainer/policy/mean Mean 0.0729202 +[id0] trainer/policy/mean Std 0.519411 +[id0] trainer/policy/mean Max 0.812271 +[id0] trainer/policy/mean Min -0.82246 +[id0] trainer/policy/std Mean 0.0504347 +[id0] trainer/policy/std Std 4.48409e-06 +[id0] trainer/policy/std Max 0.0504402 +[id0] trainer/policy/std Min 0.0504286 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.000429373 +[id0] trainer/Advantage Weights Max 0.20049 +[id0] trainer/Advantage Weights Min 0.199444 +[id0] trainer/Advantage Score Mean 0.00394787 +[id0] trainer/Advantage Score Std 0.0042947 +[id0] trainer/Advantage Score Max 0.00884658 +[id0] trainer/Advantage Score Min -0.00161982 +[id0] expl/num steps total 310 +[id0] expl/num paths total 4 +[id0] expl/path length Mean 100 +[id0] expl/path length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean -0.00147345 +[id0] expl/Rewards Std 0.101054 +[id0] expl/Rewards Max 0.285883 +[id0] expl/Rewards Min -0.214861 +[id0] expl/Returns Mean -0.147345 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max -0.147345 +[id0] expl/Returns Min -0.147345 +[id0] expl/Actions Mean 0.0274523 +[id0] expl/Actions Std 0.066725 +[id0] expl/Actions Max 0.254041 +[id0] expl/Actions Min -0.163544 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns -0.147345 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.336516 +[id0] time/evaluation sampling (s) 0.0889928 +[id0] time/exploration sampling (s) 0.0858843 +[id0] time/global_time (s) 6.86876 +[id0] time/replay buffer data storing (s) 0.000585794 +[id0] time/saving (s) 1.62125e-05 +[id0] time/training (s) 0.160057 +[id0] epoch 2 +[id0] ----------------------------------- --------------- +[id0] ----------------------------------- --------------- +[id0] trainer/num train calls 40 +[id0] trainer/QF1 Loss 27.5874 +[id0] trainer/QF2 Loss 24.4089 +[id0] trainer/Policy Loss 7.8244 +[id0] trainer/Q1 Predictions Mean 1.43631 +[id0] trainer/Q1 Predictions Std 0.695864 +[id0] trainer/Q1 Predictions Max 2.35928 +[id0] trainer/Q1 Predictions Min 0.501491 +[id0] trainer/Q2 Predictions Mean 1.72835 +[id0] trainer/Q2 Predictions Std 0.874067 +[id0] trainer/Q2 Predictions Max 2.90332 +[id0] trainer/Q2 Predictions Min 0.604359 +[id0] trainer/Q Targets Mean 4.25011 +[id0] trainer/Q Targets Std 5.1101 +[id0] trainer/Q Targets Max 11.5322 +[id0] trainer/Q Targets Min -1.23925 +[id0] trainer/Log Pis Mean 9.25355 +[id0] trainer/Log Pis Std 0.578658 +[id0] trainer/Log Pis Max 10.16 +[id0] trainer/Log Pis Min 8.5383 +[id0] trainer/rewards Mean 4.22912 +[id0] trainer/rewards Std 5.11 +[id0] trainer/rewards Max 11.5097 +[id0] trainer/rewards Min -1.25702 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89410 +[id0] trainer/policy/mean Mean 0.236795 +[id0] trainer/policy/mean Std 0.2788 +[id0] trainer/policy/mean Max 0.742046 +[id0] trainer/policy/mean Min -0.260477 +[id0] trainer/policy/std Mean 0.0506312 +[id0] trainer/policy/std Std 1.46128e-05 +[id0] trainer/policy/std Max 0.0506556 +[id0] trainer/policy/std Min 0.0506164 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.000854139 +[id0] trainer/Advantage Weights Max 0.201628 +[id0] trainer/Advantage Weights Min 0.199138 +[id0] trainer/Advantage Score Mean 0.00716419 +[id0] trainer/Advantage Score Std 0.00852061 +[id0] trainer/Advantage Score Max 0.0233918 +[id0] trainer/Advantage Score Min -0.00145853 +[id0] expl/num steps total 410 +[id0] expl/num paths total 5 +[id0] expl/path length Mean 100 +[id0] expl/path length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean -0.0170608 +[id0] expl/Rewards Std 0.10016 +[id0] expl/Rewards Max 0.198638 +[id0] expl/Rewards Min -0.367438 +[id0] expl/Returns Mean -1.70608 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max -1.70608 +[id0] expl/Returns Min -1.70608 +[id0] expl/Actions Mean 0.0154459 +[id0] expl/Actions Std 0.0608624 +[id0] expl/Actions Max 0.199007 +[id0] expl/Actions Min -0.158053 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns -1.70608 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.335119 +[id0] time/evaluation sampling (s) 0.0891402 +[id0] time/exploration sampling (s) 0.0856395 +[id0] time/global_time (s) 7.20678 +[id0] time/replay buffer data storing (s) 0.000579357 +[id0] time/saving (s) 1.74046e-05 +[id0] time/training (s) 0.15894 +[id0] epoch 3 +[id0] ----------------------------------- --------------- +[id0] ----------------------------------- --------------- +[id0] trainer/num train calls 50 +[id0] trainer/QF1 Loss 12.0632 +[id0] trainer/QF2 Loss 12.4672 +[id0] trainer/Policy Loss 5.22708 +[id0] trainer/Q1 Predictions Mean 3.13504 +[id0] trainer/Q1 Predictions Std 1.28296 +[id0] trainer/Q1 Predictions Max 4.55434 +[id0] trainer/Q1 Predictions Min 1.47195 +[id0] trainer/Q2 Predictions Mean 3.27497 +[id0] trainer/Q2 Predictions Std 1.37992 +[id0] trainer/Q2 Predictions Max 4.7188 +[id0] trainer/Q2 Predictions Min 1.39781 +[id0] trainer/Q Targets Mean 4.40037 +[id0] trainer/Q Targets Std 4.29655 +[id0] trainer/Q Targets Max 10.2577 +[id0] trainer/Q Targets Min -0.60194 +[id0] trainer/Log Pis Mean 8.32517 +[id0] trainer/Log Pis Std 1.02499 +[id0] trainer/Log Pis Max 9.8133 +[id0] trainer/Log Pis Min 7.10904 +[id0] trainer/rewards Mean 4.36342 +[id0] trainer/rewards Std 4.28988 +[id0] trainer/rewards Max 10.2231 +[id0] trainer/rewards Min -0.621302 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 89510 +[id0] trainer/policy/mean Mean 0.0279084 +[id0] trainer/policy/mean Std 0.513607 +[id0] trainer/policy/mean Max 0.844352 +[id0] trainer/policy/mean Min -0.899527 +[id0] trainer/policy/std Mean 0.0508098 +[id0] trainer/policy/std Std 2.20989e-05 +[id0] trainer/policy/std Max 0.05085 +[id0] trainer/policy/std Min 0.0507816 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.00161367 +[id0] trainer/Advantage Weights Max 0.202964 +[id0] trainer/Advantage Weights Min 0.198459 +[id0] trainer/Advantage Score Mean 0.0103492 +[id0] trainer/Advantage Score Std 0.0160741 +[id0] trainer/Advantage Score Max 0.0398329 +[id0] trainer/Advantage Score Min -0.00505543 +[id0] expl/num steps total 510 +[id0] expl/num paths total 6 +[id0] expl/path length Mean 100 +[id0] expl/path length Std 0 +[id0] expl/path length Max 100 +[id0] expl/path length Min 100 +[id0] expl/Rewards Mean -4.94094e-05 +[id0] expl/Rewards Std 0.117347 +[id0] expl/Rewards Max 0.283955 +[id0] expl/Rewards Min -0.282688 +[id0] expl/Returns Mean -0.00494094 +[id0] expl/Returns Std 0 +[id0] expl/Returns Max -0.00494094 +[id0] expl/Returns Min -0.00494094 +[id0] expl/Actions Mean 0.00339519 +[id0] expl/Actions Std 0.0829051 +[id0] expl/Actions Max 0.226102 +[id0] expl/Actions Min -0.219796 +[id0] expl/Num Paths 1 +[id0] expl/Average Returns -0.00494094 +[id0] eval/num steps total 0 +[id0] eval/num paths total 0 +[id0] time/epoch_time (s) 0.333216 +[id0] time/evaluation sampling (s) 0.0866637 +[id0] time/exploration sampling (s) 0.0855696 +[id0] time/global_time (s) 7.54279 +[id0] time/replay buffer data storing (s) 0.000584841 +[id0] time/saving (s) 2.0504e-05 +[id0] time/training (s) 0.159361 +[id0] epoch 4 +[id0] ----------------------------------- --------------- +[id0] ----------------------------------- ---------------- +[id0] replay_buffer/size 999999 +[id0] trainer/num train calls 10 +[id0] trainer/QF1 Loss 128.243 +[id0] trainer/QF2 Loss 128.23 +[id0] trainer/Policy Loss 9.61179 +[id0] trainer/Q1 Predictions Mean -0.00291776 +[id0] trainer/Q1 Predictions Std 0.00277899 +[id0] trainer/Q1 Predictions Max 0.0022958 +[id0] trainer/Q1 Predictions Min -0.00594651 +[id0] trainer/Q2 Predictions Mean -0.00140706 +[id0] trainer/Q2 Predictions Std 0.00304298 +[id0] trainer/Q2 Predictions Max 0.00426262 +[id0] trainer/Q2 Predictions Min -0.00494887 +[id0] trainer/Q Targets Mean 10.6334 +[id0] trainer/Q Targets Std 3.88799 +[id0] trainer/Q Targets Max 14.1475 +[id0] trainer/Q Targets Min 3.78155 +[id0] trainer/Log Pis Mean 9.60776 +[id0] trainer/Log Pis Std 1.05595 +[id0] trainer/Log Pis Max 11.3449 +[id0] trainer/Log Pis Min 8.1502 +[id0] trainer/rewards Mean 10.6422 +[id0] trainer/rewards Std 3.88682 +[id0] trainer/rewards Max 14.1504 +[id0] trainer/rewards Min 3.79215 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 999999 +[id0] trainer/policy/mean Mean -6.55544e-05 +[id0] trainer/policy/mean Std 0.000349378 +[id0] trainer/policy/mean Max 0.000451859 +[id0] trainer/policy/mean Min -0.000775964 +[id0] trainer/policy/std Mean 0.0497871 +[id0] trainer/policy/std Std 7.45058e-09 +[id0] trainer/policy/std Max 0.0497871 +[id0] trainer/policy/std Min 0.0497871 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.00124837 +[id0] trainer/Advantage Weights Max 0.201635 +[id0] trainer/Advantage Weights Min 0.198198 +[id0] trainer/Advantage Score Mean 0.000217358 +[id0] trainer/Advantage Score Std 0.000624335 +[id0] trainer/Advantage Score Max 0.00103367 +[id0] trainer/Advantage Score Min -0.000685691 +[id0] expl/num steps total 110 +[id0] expl/num paths total 3 +[id0] expl/path length Mean 50 +[id0] expl/path length Std 0 +[id0] expl/path length Max 50 +[id0] expl/path length Min 50 +[id0] expl/Rewards Mean -0.00942267 +[id0] expl/Rewards Std 0.0922913 +[id0] expl/Rewards Max 0.174907 +[id0] expl/Rewards Min -0.239428 +[id0] expl/Returns Mean -0.471134 +[id0] expl/Returns Std 0.145133 +[id0] expl/Returns Max -0.326001 +[id0] expl/Returns Min -0.616266 +[id0] expl/Actions Mean -0.00227338 +[id0] expl/Actions Std 0.0496989 +[id0] expl/Actions Max 0.12164 +[id0] expl/Actions Min -0.168367 +[id0] expl/Num Paths 2 +[id0] expl/Average Returns -0.471134 +[id0] eval/num steps total 100 +[id0] eval/num paths total 2 +[id0] eval/path length Mean 50 +[id0] eval/path length Std 0 +[id0] eval/path length Max 50 +[id0] eval/path length Min 50 +[id0] eval/Rewards Mean -0.00522769 +[id0] eval/Rewards Std 0.0275839 +[id0] eval/Rewards Max 0.039259 +[id0] eval/Rewards Min -0.103042 +[id0] eval/Returns Mean -0.261385 +[id0] eval/Returns Std 0.217907 +[id0] eval/Returns Max -0.0434773 +[id0] eval/Returns Min -0.479292 +[id0] eval/Actions Mean -1.58854e-06 +[id0] eval/Actions Std 7.79551e-06 +[id0] eval/Actions Max 6.11099e-05 +[id0] eval/Actions Min -7.72074e-05 +[id0] eval/Num Paths 2 +[id0] eval/Average Returns -0.261385 +[id0] time/epoch_time (s) 0.358147 +[id0] time/evaluation sampling (s) 0.0861511 +[id0] time/exploration sampling (s) 0.0850875 +[id0] time/global_time (s) 5.02434 +[id0] time/replay buffer data storing (s) 1.66893e-06 +[id0] time/saving (s) 0.0109243 +[id0] time/training (s) 0.161492 +[id0] epoch 0 +[id0] ----------------------------------- ---------------- +[id0] ----------------------------------- ---------------- +[id0] replay_buffer/size 999999 +[id0] trainer/num train calls 20 +[id0] trainer/QF1 Loss 130.531 +[id0] trainer/QF2 Loss 130.508 +[id0] trainer/Policy Loss 9.74677 +[id0] trainer/Q1 Predictions Mean 0.191695 +[id0] trainer/Q1 Predictions Std 0.045567 +[id0] trainer/Q1 Predictions Max 0.26741 +[id0] trainer/Q1 Predictions Min 0.137171 +[id0] trainer/Q2 Predictions Mean 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time/saving (s) 1.7643e-05 +[id0] time/training (s) 0.156221 +[id0] epoch 1 +[id0] ----------------------------------- ---------------- +[id0] ----------------------------------- ---------------- +[id0] replay_buffer/size 999999 +[id0] trainer/num train calls 30 +[id0] trainer/QF1 Loss 149.386 +[id0] trainer/QF2 Loss 148.022 +[id0] trainer/Policy Loss 9.06901 +[id0] trainer/Q1 Predictions Mean 0.749219 +[id0] trainer/Q1 Predictions Std 0.10096 +[id0] trainer/Q1 Predictions Max 0.907284 +[id0] trainer/Q1 Predictions Min 0.612129 +[id0] trainer/Q2 Predictions Mean 0.802964 +[id0] trainer/Q2 Predictions Std 0.109019 +[id0] trainer/Q2 Predictions Max 0.951639 +[id0] trainer/Q2 Predictions Min 0.657669 +[id0] trainer/Q Targets Mean 12.9371 +[id0] trainer/Q Targets Std 0.914629 +[id0] trainer/Q Targets Max 14.2033 +[id0] trainer/Q Targets Min 11.5724 +[id0] trainer/Log Pis Mean 9.81235 +[id0] trainer/Log Pis Std 2.16973 +[id0] trainer/Log Pis Max 12.1621 +[id0] trainer/Log Pis Min 5.89467 +[id0] trainer/rewards Mean 12.9359 +[id0] trainer/rewards Std 0.916861 +[id0] trainer/rewards Max 14.2042 +[id0] trainer/rewards Min 11.5669 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 999999 +[id0] trainer/policy/mean Mean 0.00699736 +[id0] trainer/policy/mean Std 0.274558 +[id0] trainer/policy/mean Max 0.502824 +[id0] trainer/policy/mean Min -0.501869 +[id0] trainer/policy/std Mean 0.0502163 +[id0] trainer/policy/std Std 7.33919e-06 +[id0] trainer/policy/std Max 0.0502249 +[id0] trainer/policy/std Min 0.0502034 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.0100934 +[id0] trainer/Advantage Weights Max 0.215105 +[id0] trainer/Advantage Weights Min 0.18757 +[id0] trainer/Advantage Score Mean 0.00527165 +[id0] trainer/Advantage Score Std 0.00503921 +[id0] trainer/Advantage Score Max 0.0126795 +[id0] trainer/Advantage Score Min -0.00101775 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Mean 0.667121 +[id0] eval/Returns Std 0.065338 +[id0] eval/Returns Max 0.732459 +[id0] eval/Returns Min 0.601783 +[id0] eval/Actions Mean -0.00266409 +[id0] eval/Actions Std 0.00909245 +[id0] eval/Actions Max 0.063407 +[id0] eval/Actions Min -0.0650911 +[id0] eval/Num Paths 2 +[id0] eval/Average Returns 0.667121 +[id0] time/epoch_time (s) 0.327734 +[id0] time/evaluation sampling (s) 0.0886054 +[id0] time/exploration sampling (s) 0.0836575 +[id0] time/global_time (s) 5.69537 +[id0] time/replay buffer data storing (s) 1.43051e-06 +[id0] time/saving (s) 1.66893e-05 +[id0] time/training (s) 0.153701 +[id0] epoch 2 +[id0] ----------------------------------- ---------------- +[id0] ----------------------------------- ---------------- +[id0] replay_buffer/size 999999 +[id0] trainer/num train calls 40 +[id0] trainer/QF1 Loss 119.908 +[id0] trainer/QF2 Loss 118.723 +[id0] trainer/Policy Loss 8.32754 +[id0] trainer/Q1 Predictions Mean 1.56108 +[id0] trainer/Q1 Predictions Std 0.267839 +[id0] 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-0.822065 +[id0] trainer/policy/std Mean 0.0504076 +[id0] trainer/policy/std Std 1.03933e-05 +[id0] trainer/policy/std Max 0.0504229 +[id0] trainer/policy/std Min 0.0503897 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.0129541 +[id0] trainer/Advantage Weights Max 0.223779 +[id0] trainer/Advantage Weights Min 0.189477 +[id0] trainer/Advantage Score Mean 0.00499592 +[id0] trainer/Advantage Score Std 0.00628837 +[id0] trainer/Advantage Score Max 0.0164317 +[id0] trainer/Advantage Score Min -0.000207543 +[id0] expl/num steps total 410 +[id0] expl/num paths total 9 +[id0] expl/path length Mean 50 +[id0] expl/path length Std 0 +[id0] expl/path length Max 50 +[id0] expl/path length Min 50 +[id0] expl/Rewards Mean -0.0133282 +[id0] expl/Rewards Std 0.0818791 +[id0] expl/Rewards Max 0.143393 +[id0] expl/Rewards Min -0.218871 +[id0] expl/Returns Mean -0.666409 +[id0] expl/Returns Std 0.341871 +[id0] expl/Returns Max -0.324538 +[id0] expl/Returns Min -1.00828 +[id0] expl/Actions Mean -0.00165292 +[id0] expl/Actions Std 0.0553495 +[id0] expl/Actions Max 0.135908 +[id0] expl/Actions Min -0.20795 +[id0] expl/Num Paths 2 +[id0] expl/Average Returns -0.666409 +[id0] eval/num steps total 400 +[id0] eval/num paths total 8 +[id0] eval/path length Mean 50 +[id0] eval/path length Std 0 +[id0] eval/path length Max 50 +[id0] eval/path length Min 50 +[id0] eval/Rewards Mean -0.0299146 +[id0] eval/Rewards Std 0.0794842 +[id0] eval/Rewards Max 0.0553235 +[id0] eval/Rewards Min -0.380774 +[id0] eval/Returns Mean -1.49573 +[id0] eval/Returns Std 0.0948055 +[id0] eval/Returns Max -1.40092 +[id0] eval/Returns Min -1.59053 +[id0] eval/Actions Mean -0.00313139 +[id0] eval/Actions Std 0.0241297 +[id0] eval/Actions Max 0.187418 +[id0] eval/Actions Min -0.186022 +[id0] eval/Num Paths 2 +[id0] eval/Average Returns -1.49573 +[id0] time/epoch_time (s) 0.325631 +[id0] time/evaluation sampling (s) 0.0860143 +[id0] time/exploration sampling (s) 0.0823944 +[id0] time/global_time (s) 6.02453 +[id0] time/replay buffer data storing (s) 1.90735e-06 +[id0] time/saving (s) 1.71661e-05 +[id0] time/training (s) 0.155682 +[id0] epoch 3 +[id0] ----------------------------------- ---------------- +[id0] ----------------------------------- ---------------- +[id0] replay_buffer/size 999999 +[id0] trainer/num train calls 50 +[id0] trainer/QF1 Loss 83.3431 +[id0] trainer/QF2 Loss 81.4827 +[id0] trainer/Policy Loss 3.89155 +[id0] trainer/Q1 Predictions Mean 4.05625 +[id0] trainer/Q1 Predictions Std 1.02679 +[id0] trainer/Q1 Predictions Max 5.70364 +[id0] trainer/Q1 Predictions Min 2.55732 +[id0] trainer/Q2 Predictions Mean 4.16253 +[id0] trainer/Q2 Predictions Std 0.902176 +[id0] trainer/Q2 Predictions Max 5.622 +[id0] trainer/Q2 Predictions Min 2.97098 +[id0] trainer/Q Targets Mean 13.1126 +[id0] trainer/Q Targets Std 0.936574 +[id0] trainer/Q Targets Max 14.1554 +[id0] trainer/Q Targets Min 11.5023 +[id0] trainer/Log Pis Mean 7.86051 +[id0] trainer/Log Pis Std 2.51712 +[id0] trainer/Log Pis Max 11.5211 +[id0] trainer/Log Pis Min 4.43826 +[id0] trainer/rewards Mean 13.0868 +[id0] trainer/rewards Std 0.936715 +[id0] trainer/rewards Max 14.1353 +[id0] trainer/rewards Min 11.4806 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 999999 +[id0] trainer/policy/mean Mean -0.12492 +[id0] trainer/policy/mean Std 0.583764 +[id0] trainer/policy/mean Max 0.901146 +[id0] trainer/policy/mean Min -0.97746 +[id0] trainer/policy/std Mean 0.0505768 +[id0] trainer/policy/std Std 4.37533e-05 +[id0] trainer/policy/std Max 0.0506402 +[id0] trainer/policy/std Min 0.0504943 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.0324323 +[id0] trainer/Advantage Weights Max 0.23064 +[id0] trainer/Advantage Weights Min 0.144645 +[id0] trainer/Advantage Score Mean 0.000714397 +[id0] trainer/Advantage Score Std 0.017543 +[id0] trainer/Advantage Score Max 0.0164287 +[id0] trainer/Advantage Score Min -0.0302291 +[id0] expl/num steps total 510 +[id0] expl/num paths total 11 +[id0] expl/path length Mean 50 +[id0] expl/path length Std 0 +[id0] expl/path length Max 50 +[id0] expl/path length Min 50 +[id0] expl/Rewards Mean -0.0157396 +[id0] expl/Rewards Std 0.113587 +[id0] expl/Rewards Max 0.200774 +[id0] expl/Rewards Min -0.359458 +[id0] expl/Returns Mean -0.786981 +[id0] expl/Returns Std 0.230931 +[id0] expl/Returns Max -0.55605 +[id0] expl/Returns Min -1.01791 +[id0] expl/Actions Mean -0.0133395 +[id0] expl/Actions Std 0.0557228 +[id0] expl/Actions Max 0.163531 +[id0] expl/Actions Min -0.168684 +[id0] expl/Num Paths 2 +[id0] expl/Average Returns -0.786981 +[id0] eval/num steps total 500 +[id0] eval/num paths total 10 +[id0] eval/path length Mean 50 +[id0] eval/path length Std 0 +[id0] eval/path length Max 50 +[id0] eval/path length Min 50 +[id0] eval/Rewards Mean -0.00575395 +[id0] eval/Rewards Std 0.064172 +[id0] eval/Rewards Max 0.178785 +[id0] eval/Rewards Min -0.279795 +[id0] eval/Returns Mean -0.287698 +[id0] eval/Returns Std 0.444451 +[id0] eval/Returns Max 0.156753 +[id0] eval/Returns Min -0.732149 +[id0] eval/Actions Mean -0.00564779 +[id0] eval/Actions Std 0.0149998 +[id0] eval/Actions Max 0.128229 +[id0] eval/Actions Min -0.119282 +[id0] eval/Num Paths 2 +[id0] eval/Average Returns -0.287698 +[id0] time/epoch_time (s) 0.289075 +[id0] time/evaluation sampling (s) 0.0844147 +[id0] time/exploration sampling (s) 0.0835559 +[id0] time/global_time (s) 6.31662 +[id0] time/replay buffer data storing (s) 2.6226e-06 +[id0] time/saving (s) 1.57356e-05 +[id0] time/training (s) 0.119599 +[id0] epoch 4 +[id0] ----------------------------------- ---------------- diff --git a/tests/regression/awac/d4rl/id0/pretrain_policy.csv b/tests/regression/awac/d4rl/id0/pretrain_policy.csv new file mode 100644 index 000000000..e69de29bb diff --git a/tests/regression/awac/d4rl/id0/pretrain_q.csv b/tests/regression/awac/d4rl/id0/pretrain_q.csv new file mode 100644 index 000000000..e69de29bb diff --git a/tests/regression/awac/d4rl/id0/progress.csv b/tests/regression/awac/d4rl/id0/progress.csv new file mode 100644 index 000000000..0fa3e1bd9 --- /dev/null +++ b/tests/regression/awac/d4rl/id0/progress.csv @@ -0,0 +1,6 @@ +epoch,eval/Actions Max,eval/Actions Mean,eval/Actions Min,eval/Actions Std,eval/Average Returns,eval/Num Paths,eval/Returns Max,eval/Returns Mean,eval/Returns Min,eval/Returns Std,eval/Rewards Max,eval/Rewards Mean,eval/Rewards Min,eval/Rewards Std,eval/num paths total,eval/num steps total,eval/path length Max,eval/path length Mean,eval/path length Min,eval/path length Std,expl/Actions Max,expl/Actions Mean,expl/Actions Min,expl/Actions Std,expl/Average Returns,expl/Num Paths,expl/Returns Max,expl/Returns Mean,expl/Returns Min,expl/Returns Std,expl/Rewards Max,expl/Rewards Mean,expl/Rewards Min,expl/Rewards Std,expl/num paths total,expl/num steps total,expl/path 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diff --git a/tests/regression/awac/d4rl/id0/script_name.txt b/tests/regression/awac/d4rl/id0/script_name.txt new file mode 100644 index 000000000..d556b8ec8 --- /dev/null +++ b/tests/regression/awac/d4rl/id0/script_name.txt @@ -0,0 +1 @@ +tests/regression/awac/d4rl/test_d4rl.py \ No newline at end of file diff --git a/tests/regression/awac/d4rl/id0/variant.json b/tests/regression/awac/d4rl/id0/variant.json new file mode 100644 index 000000000..1838dde17 --- /dev/null +++ b/tests/regression/awac/d4rl/id0/variant.json @@ -0,0 +1,96 @@ +{ + "add_env_demos": false, + "add_env_offpolicy_data": false, + "algo_kwargs": { + "batch_size": 5, + "min_num_steps_before_training": 10, + "num_epochs": 5, + "num_eval_steps_per_epoch": 100, + "num_expl_steps_per_train_loop": 100, + "num_trains_per_train_loop": 10, + "offline_rl": true + }, + "algorithm": "SAC", + "collection_mode": "batch", + "debug": true, + "env_id": "halfcheetah-expert-v0", + "exp_id": 0, + "exp_name": "references/awac/d4rl/d4rl_offline", + "layer_size": 256, + "load_demos": false, + "load_env_dataset_demos": true, + "logger_config": { + "run_id": 0, + "snapshot_gap": 100, + "snapshot_mode": "gap" + }, + "max_path_length": 50, + "normalize_env": false, + "path_loader_class": { + "$class": "rlkit.demos.source.hdf5_path_loader.HDF5PathLoader" + }, + "path_loader_kwargs": {}, + "policy_class": { + "$class": "rlkit.torch.sac.policies.gaussian_policy.GaussianPolicy" + }, + "policy_kwargs": { + "hidden_sizes": [ + 256, + 256, + 256, + 256 + ], + "max_log_std": 0, + "min_log_std": -6, + "std": null, + "std_architecture": "values" + }, + "pretrain_policy": true, + "pretrain_rl": true, + "qf_kwargs": { + "hidden_sizes": [ + 256, + 256 + ] + }, + "replay_buffer_size": 2000000, + "run_id": 0, + "seed": "0", + "seedid": 0, + "trainer_kwargs": { + "alpha": 0, + "awr_min_q": true, + "awr_sample_actions": false, + "awr_use_mle_for_vf": true, + "bc_num_pretrain_steps": 0, + "beta": 0.1, + "compute_bc": false, + "discount": 0.99, + "normalize_over_state": "advantage", + "policy_lr": 0.0003, + "policy_weight_decay": 0.0001, + "post_bc_pretrain_hyperparams": { + "bc_weight": 0.0, + "compute_bc": false + }, + "q_num_pretrain1_steps": 0, + "q_num_pretrain2_steps": 0, + "q_weight_decay": 0, + "qf_lr": 0.0003, + "reparam_weight": 0.0, + "reward_scale": 1, + "reward_transform_kwargs": null, + "rl_weight": 1.0, + "soft_target_tau": 0.005, + "target_update_period": 1, + "terminal_transform_kwargs": null, + "use_automatic_entropy_tuning": false, + "use_awr_update": true, + "use_reparam_update": false, + "validation_qlearning": false + }, + "trial_name": "id0", + "unique_id": "a27a7428-54c4-4a35-b16f-20fff27a8c9d", + "use_validation_buffer": false, + "version": "normal" +} \ No newline at end of file diff --git a/tests/regression/awac/d4rl/test_d4rl.py b/tests/regression/awac/d4rl/test_d4rl.py new file mode 100644 index 000000000..441a27b11 --- /dev/null +++ b/tests/regression/awac/d4rl/test_d4rl.py @@ -0,0 +1,29 @@ +"""Test AWAC on Mujoco benchmark tasks.""" +import os +import sys + +from examples.awac.d4rl.d4rl_offline import main + +from rlkit.core import logger +from rlkit.testing import csv_util + +def test_awac_d4rl(): + cmd = "python examples/awac/d4rl/d4rl_offline.py --1 --local --gpu --run 0 --seed 0 --debug" + sys.argv = cmd.split(" ")[1:] + main() + + # check if training results match + reference_csv = "tests/regression/awac/d4rl/id0/progress.csv" + output_csv = os.path.join(logger.get_snapshot_dir(), "progress.csv") + output = csv_util.get_exp(output_csv) + reference = csv_util.get_exp(reference_csv) + keys = ["epoch", "trainer/Advantage Score Max", "trainer/Q1 Predictions Mean", "trainer/replay_buffer_len"] + csv_util.check_equal(reference, output, keys) + + # TODO: this test seems to have some extra stochasticity to control, perhaps from the env? + # this doesn't affect training because offline_rl is set to True + # keys = ["expl/Average Returns", ] + # csv_util.check_equal(reference, output, keys) + +if __name__ == "__main__": + test_awac_d4rl() diff --git a/tests/regression/awac/gcrl/__init__.py b/tests/regression/awac/gcrl/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/regression/awac/gcrl/gcrl_data_mini/id0/debug.log b/tests/regression/awac/gcrl/gcrl_data_mini/id0/debug.log new file mode 100644 index 000000000..b1c2c26eb --- /dev/null +++ b/tests/regression/awac/gcrl/gcrl_data_mini/id0/debug.log @@ -0,0 +1,19800 @@ +[id0] ------------------------------ ----------- +[id0] pretrain_bc/batch 0 +[id0] pretrain_bc/Train Logprob Loss 105.981 +[id0] pretrain_bc/Test Logprob Loss 79.2191 +[id0] pretrain_bc/Train MSE 0.275881 +[id0] pretrain_bc/Test MSE 0.207349 +[id0] pretrain_bc/train_policy_loss 0 +[id0] pretrain_bc/test_policy_loss 0 +[id0] pretrain_bc/epoch_time 0.0161014 +[id0] ------------------------------ ----------- +[id0] ------------------------------ ----------- +[id0] pretrain_bc/batch 1000 +[id0] pretrain_bc/Train Logprob Loss 115.172 +[id0] pretrain_bc/Test Logprob Loss 70.5157 +[id0] pretrain_bc/Train MSE 0.296191 +[id0] pretrain_bc/Test MSE 0.186141 +[id0] pretrain_bc/train_policy_loss 0 +[id0] pretrain_bc/test_policy_loss 0 +[id0] pretrain_bc/epoch_time 10.3987 +[id0] ------------------------------ ----------- +[id0] ------------------------------ ----------- +[id0] pretrain_bc/batch 2000 +[id0] pretrain_bc/Train Logprob Loss 93.4342 +[id0] pretrain_bc/Test Logprob Loss 67.6947 +[id0] pretrain_bc/Train MSE 0.245202 +[id0] pretrain_bc/Test MSE 0.18297 +[id0] pretrain_bc/train_policy_loss 0 +[id0] pretrain_bc/test_policy_loss 0 +[id0] pretrain_bc/epoch_time 10.2962 +[id0] ------------------------------ ----------- +[id0] ------------------------------ ----------- +[id0] pretrain_bc/batch 3000 +[id0] pretrain_bc/Train Logprob Loss 108.079 +[id0] pretrain_bc/Test Logprob Loss 48.5572 +[id0] pretrain_bc/Train MSE 0.28337 +[id0] pretrain_bc/Test MSE 0.134345 +[id0] pretrain_bc/train_policy_loss 0 +[id0] pretrain_bc/test_policy_loss 0 +[id0] pretrain_bc/epoch_time 10.3688 +[id0] ------------------------------ ----------- +[id0] ------------------------------ ----------- +[id0] pretrain_bc/batch 4000 +[id0] pretrain_bc/Train Logprob Loss 80.8695 +[id0] pretrain_bc/Test Logprob Loss 46.1981 +[id0] pretrain_bc/Train MSE 0.217755 +[id0] pretrain_bc/Test MSE 0.125708 +[id0] pretrain_bc/train_policy_loss 0 +[id0] pretrain_bc/test_policy_loss 0 +[id0] pretrain_bc/epoch_time 10.2212 +[id0] ------------------------------ ----------- +[id0] ------------------------------ ----------- +[id0] pretrain_bc/batch 5000 +[id0] pretrain_bc/Train Logprob Loss 89.1218 +[id0] pretrain_bc/Test Logprob Loss 43.5657 +[id0] pretrain_bc/Train MSE 0.239171 +[id0] pretrain_bc/Test MSE 0.120928 +[id0] pretrain_bc/train_policy_loss 0 +[id0] pretrain_bc/test_policy_loss 0 +[id0] pretrain_bc/epoch_time 9.88116 +[id0] ------------------------------ ----------- +[id0] ------------------------------ ----------- +[id0] pretrain_bc/batch 6000 +[id0] pretrain_bc/Train Logprob Loss 89.5745 +[id0] pretrain_bc/Test Logprob Loss 44.2766 +[id0] pretrain_bc/Train MSE 0.23628 +[id0] pretrain_bc/Test MSE 0.119211 +[id0] pretrain_bc/train_policy_loss 0 +[id0] pretrain_bc/test_policy_loss 0 +[id0] pretrain_bc/epoch_time 9.89918 +[id0] ------------------------------ ----------- +[id0] ------------------------------ ----------- +[id0] pretrain_bc/batch 7000 +[id0] pretrain_bc/Train Logprob Loss 110.626 +[id0] pretrain_bc/Test Logprob Loss 68.9353 +[id0] pretrain_bc/Train MSE 0.284074 +[id0] pretrain_bc/Test MSE 0.180449 +[id0] pretrain_bc/train_policy_loss 0 +[id0] pretrain_bc/test_policy_loss 0 +[id0] pretrain_bc/epoch_time 9.8566 +[id0] ------------------------------ ----------- +[id0] ------------------------------ ----------- +[id0] pretrain_bc/batch 8000 +[id0] pretrain_bc/Train Logprob Loss 93.0084 +[id0] pretrain_bc/Test Logprob Loss 49.9702 +[id0] pretrain_bc/Train MSE 0.240886 +[id0] pretrain_bc/Test MSE 0.138114 +[id0] pretrain_bc/train_policy_loss 0 +[id0] pretrain_bc/test_policy_loss 0 +[id0] pretrain_bc/epoch_time 10.1677 +[id0] ------------------------------ ----------- +[id0] ------------------------------ ----------- +[id0] pretrain_bc/batch 9000 +[id0] pretrain_bc/Train Logprob Loss 113.357 +[id0] pretrain_bc/Test Logprob Loss 61.1109 +[id0] pretrain_bc/Train MSE 0.287341 +[id0] pretrain_bc/Test MSE 0.161471 +[id0] pretrain_bc/train_policy_loss 0 +[id0] pretrain_bc/test_policy_loss 0 +[id0] pretrain_bc/epoch_time 9.90812 +[id0] ------------------------------ ----------- +[id0] ------------------------------------------------ -------------- +[id0] trainer/num train calls 1000 +[id0] trainer/QF1 Loss 0.0274527 +[id0] trainer/QF2 Loss 0.0273505 +[id0] trainer/Policy Loss 3.12861 +[id0] trainer/Q1 Predictions Mean 0.000460684 +[id0] trainer/Q1 Predictions Std 0.000195132 +[id0] trainer/Q1 Predictions Max 0.00112377 +[id0] trainer/Q1 Predictions Min -1.4027e-05 +[id0] trainer/Q2 Predictions Mean -0.000160566 +[id0] trainer/Q2 Predictions Std 0.000176723 +[id0] trainer/Q2 Predictions Max 0.000385734 +[id0] trainer/Q2 Predictions Min -0.000537722 +[id0] trainer/Q Targets Mean -0.0948818 +[id0] trainer/Q Targets Std 0.135519 +[id0] trainer/Q Targets Max -0.000268238 +[id0] trainer/Q Targets Min -0.704677 +[id0] trainer/Log Pis Mean 3.12838 +[id0] trainer/Log Pis Std 1.02984 +[id0] trainer/Log Pis Max 4.16019 +[id0] trainer/Log Pis Min -2.2165 +[id0] trainer/policy/mean Mean 0 +[id0] trainer/policy/mean Std 0 +[id0] trainer/policy/mean Max 0 +[id0] trainer/policy/mean Min 0 +[id0] trainer/policy/std Mean 0.0497871 +[id0] trainer/policy/std Std 3.72529e-09 +[id0] trainer/policy/std Max 0.0497871 +[id0] trainer/policy/std Min 0.0497871 +[id0] trainer/policy/log_std Mean -3 +[id0] trainer/policy/log_std Std 0 +[id0] trainer/policy/log_std Max -3 +[id0] trainer/policy/log_std Min -3 +[id0] trainer/policy/entropy Mean -3.16212 +[id0] trainer/policy/entropy Std 4.76837e-07 +[id0] trainer/policy/entropy Max -3.16212 +[id0] trainer/policy/entropy Min -3.16212 +[id0] trainer/Advantage Weights Mean 0.000976562 +[id0] trainer/Advantage Weights Std 1.94005e-07 +[id0] trainer/Advantage Weights Max 0.000977228 +[id0] trainer/Advantage Weights Min 0.000976019 +[id0] trainer/Advantage Score Mean 0.00069399 +[id0] trainer/Advantage Score Std 0.000198645 +[id0] trainer/Advantage Score Max 0.00137571 +[id0] trainer/Advantage Score Min 0.00013719 +[id0] trainer/bc/Train Logprob Loss 91.5713 +[id0] trainer/bc/Test Logprob Loss 53.3808 +[id0] trainer/bc/Train MSE 0.243058 +[id0] trainer/bc/Test MSE 0.143041 +[id0] trainer/bc/train_policy_loss 91.5713 +[id0] trainer/bc/test_policy_loss 53.3808 +[id0] exploration/num steps total 2000 +[id0] exploration/num paths total 10 +[id0] exploration/path length Mean 200 +[id0] exploration/path length Std 0 +[id0] 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diff --git a/tests/regression/awac/gcrl/gcrl_data_mini/id0/script_name.txt b/tests/regression/awac/gcrl/gcrl_data_mini/id0/script_name.txt new file mode 100644 index 000000000..3473b49f5 --- /dev/null +++ b/tests/regression/awac/gcrl/gcrl_data_mini/id0/script_name.txt @@ -0,0 +1 @@ +experiments/ashvin/icml2020/pusher/state2/random2.py \ No newline at end of file diff --git a/tests/regression/awac/gcrl/gcrl_data_mini/id0/variant.json b/tests/regression/awac/gcrl/gcrl_data_mini/id0/variant.json new file mode 100644 index 000000000..9d16a99f2 --- /dev/null +++ b/tests/regression/awac/gcrl/gcrl_data_mini/id0/variant.json @@ -0,0 +1,156 @@ +{ + "achieved_goal_key": "state_achieved_goal", + "add_env_demos": false, + "add_env_offpolicy_data": false, + "algorithm": "SAC", + "batch_size": 1024, + "collection_mode": "batch", + "desired_goal_key": "state_desired_goal", + "doodad_info": { + "EC2_instance_id": "i-0aabeb951e5af9b88", + "aws_ami_id": null, + "base_exp_name": 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0.000263562 +[id0] trainer/Q1 Predictions Max -2.54349e-05 +[id0] trainer/Q1 Predictions Min -0.00114263 +[id0] trainer/Q2 Predictions Mean -0.000438495 +[id0] trainer/Q2 Predictions Std 0.000220916 +[id0] trainer/Q2 Predictions Max 4.79189e-05 +[id0] trainer/Q2 Predictions Min -0.000895643 +[id0] trainer/Q Targets Mean -0.124641 +[id0] trainer/Q Targets Std 0.106343 +[id0] trainer/Q Targets Max 0.000171593 +[id0] trainer/Q Targets Min -0.380185 +[id0] trainer/Log Pis Mean 3.14974 +[id0] trainer/Log Pis Std 1.1 +[id0] trainer/Log Pis Max 4.15625 +[id0] trainer/Log Pis Min -1.85168 +[id0] trainer/rewards Mean -0.124673 +[id0] trainer/rewards Std 0.106319 +[id0] trainer/rewards Max 0 +[id0] trainer/rewards Min -0.380129 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 90000 +[id0] trainer/policy/mean Mean -3.94223e-05 +[id0] trainer/policy/mean Std 5.48776e-06 +[id0] trainer/policy/mean Max -2.87341e-05 +[id0] trainer/policy/mean Min -5.66508e-05 +[id0] trainer/policy/std Mean 0.0497949 +[id0] trainer/policy/std Std 7.06956e-06 +[id0] trainer/policy/std Max 0.049803 +[id0] trainer/policy/std Min 0.0497869 +[id0] trainer/Advantage Weights Mean 0.0078125 +[id0] trainer/Advantage Weights Std 0.00198469 +[id0] trainer/Advantage Weights Max 0.0130013 +[id0] trainer/Advantage Weights Min 0.00423533 +[id0] trainer/Advantage Score Mean -0.000101316 +[id0] trainer/Advantage Score Std 0.000263755 +[id0] trainer/Advantage Score Max 0.000441706 +[id0] trainer/Advantage Score Min -0.000679884 +[id0] trainer/bc/Train Logprob Loss 254.926 +[id0] trainer/bc/Test Logprob Loss 270.137 +[id0] trainer/bc/Train MSE 0.642499 +[id0] trainer/bc/Test MSE 0.689321 +[id0] trainer/bc/train_policy_loss 254.926 +[id0] trainer/bc/test_policy_loss 270.137 +[id0] trainer/batch 0 +[id0] trainer/epoch_time 0.0379837 +[id0] ------------------------------ --------------- +[id0] --------------------------------------- --------------- +[id0] trainer/num train calls 10 +[id0] trainer/QF1 Loss 0.0250762 +[id0] trainer/QF2 Loss 0.0254459 +[id0] trainer/Policy Loss 3.42755 +[id0] trainer/Q1 Predictions Mean -0.02271 +[id0] trainer/Q1 Predictions Std 0.0013751 +[id0] trainer/Q1 Predictions Max -0.020538 +[id0] trainer/Q1 Predictions Min -0.0247885 +[id0] trainer/Q2 Predictions Mean -0.0215133 +[id0] trainer/Q2 Predictions Std 0.00105855 +[id0] trainer/Q2 Predictions Max -0.0195664 +[id0] trainer/Q2 Predictions Min -0.0226589 +[id0] trainer/Q Targets Mean -0.136798 +[id0] trainer/Q Targets Std 0.110915 +[id0] trainer/Q Targets Max -0.00019019 +[id0] trainer/Q Targets Min -0.317915 +[id0] trainer/Log Pis Mean 3.40936 +[id0] trainer/Log Pis Std 0.616096 +[id0] trainer/Log Pis Max 3.98308 +[id0] trainer/Log Pis Min 2.43023 +[id0] trainer/rewards Mean -0.136573 +[id0] trainer/rewards Std 0.110873 +[id0] trainer/rewards Max -1.48851e-06 +[id0] trainer/rewards Min 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touch_distance Mean 0.229711 +[id0] expl/Final touch_distance Std 0.048363 +[id0] expl/Final touch_distance Max 0.278074 +[id0] expl/Final touch_distance Min 0.181348 +[id0] expl/hand_success Mean 0.5 +[id0] expl/hand_success Std 0.5 +[id0] expl/hand_success Max 1 +[id0] expl/hand_success Min 0 +[id0] expl/Final hand_success Mean 0.5 +[id0] expl/Final hand_success Std 0.5 +[id0] expl/Final hand_success Max 1 +[id0] expl/Final hand_success Min 0 +[id0] expl/hand_success_2 Mean 0.5 +[id0] expl/hand_success_2 Std 0.5 +[id0] expl/hand_success_2 Max 1 +[id0] expl/hand_success_2 Min 0 +[id0] expl/Final hand_success_2 Mean 0.5 +[id0] expl/Final hand_success_2 Std 0.5 +[id0] expl/Final hand_success_2 Max 1 +[id0] expl/Final hand_success_2 Min 0 +[id0] expl/hand_success_3 Mean 0.97 +[id0] expl/hand_success_3 Std 0.170587 +[id0] expl/hand_success_3 Max 1 +[id0] expl/hand_success_3 Min 0 +[id0] expl/Final hand_success_3 Mean 0.5 +[id0] expl/Final hand_success_3 Std 0.5 +[id0] expl/Final 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0.322017 +[id0] expl/puck_distance Min 0.0723456 +[id0] expl/Final puck_distance Mean 0.197181 +[id0] expl/Final puck_distance Std 0.124836 +[id0] expl/Final puck_distance Max 0.322017 +[id0] expl/Final puck_distance Min 0.0723456 +[id0] expl/state_distance Mean 0.256835 +[id0] expl/state_distance Std 0.102479 +[id0] expl/state_distance Max 0.359867 +[id0] expl/state_distance Min 0.133308 +[id0] expl/Final state_distance Mean 0.26792 +[id0] expl/Final state_distance Std 0.0914635 +[id0] expl/Final state_distance Max 0.359383 +[id0] expl/Final state_distance Min 0.176456 +[id0] expl/touch_distance Mean 0.18962 +[id0] expl/touch_distance Std 0.108421 +[id0] expl/touch_distance Max 0.317678 +[id0] expl/touch_distance Min 0.0735865 +[id0] expl/Final touch_distance Mean 0.184087 +[id0] expl/Final touch_distance Std 0.0925809 +[id0] expl/Final touch_distance Max 0.276668 +[id0] expl/Final touch_distance Min 0.0915062 +[id0] expl/hand_success Mean 0 +[id0] expl/hand_success Std 0 +[id0] expl/hand_success Max 0 +[id0] expl/hand_success Min 0 +[id0] expl/Final hand_success Mean 0 +[id0] expl/Final hand_success Std 0 +[id0] expl/Final hand_success Max 0 +[id0] expl/Final hand_success Min 0 +[id0] expl/hand_success_2 Mean 0 +[id0] expl/hand_success_2 Std 0 +[id0] expl/hand_success_2 Max 0 +[id0] expl/hand_success_2 Min 0 +[id0] expl/Final hand_success_2 Mean 0 +[id0] expl/Final hand_success_2 Std 0 +[id0] expl/Final hand_success_2 Max 0 +[id0] expl/Final hand_success_2 Min 0 +[id0] expl/hand_success_3 Mean 0.08 +[id0] expl/hand_success_3 Std 0.271293 +[id0] expl/hand_success_3 Max 1 +[id0] expl/hand_success_3 Min 0 +[id0] expl/Final hand_success_3 Mean 0 +[id0] expl/Final hand_success_3 Std 0 +[id0] expl/Final hand_success_3 Max 0 +[id0] expl/Final hand_success_3 Min 0 +[id0] expl/puck_success Mean 0 +[id0] expl/puck_success Std 0 +[id0] expl/puck_success Max 0 +[id0] expl/puck_success Min 0 +[id0] expl/Final puck_success Mean 0 +[id0] expl/Final puck_success Std 0 +[id0] expl/Final puck_success Max 0 +[id0] expl/Final puck_success Min 0 +[id0] expl/puck_success_2 Mean 0.5 +[id0] expl/puck_success_2 Std 0.5 +[id0] expl/puck_success_2 Max 1 +[id0] expl/puck_success_2 Min 0 +[id0] expl/Final puck_success_2 Mean 0.5 +[id0] expl/Final puck_success_2 Std 0.5 +[id0] expl/Final puck_success_2 Max 1 +[id0] expl/Final puck_success_2 Min 0 +[id0] expl/puck_success_3 Mean 0.5 +[id0] expl/puck_success_3 Std 0.5 +[id0] expl/puck_success_3 Max 1 +[id0] expl/puck_success_3 Min 0 +[id0] expl/Final puck_success_3 Mean 0.5 +[id0] expl/Final puck_success_3 Std 0.5 +[id0] expl/Final puck_success_3 Max 1 +[id0] expl/Final puck_success_3 Min 0 +[id0] expl/hand_and_puck_success Mean 0 +[id0] expl/hand_and_puck_success Std 0 +[id0] expl/hand_and_puck_success Max 0 +[id0] expl/hand_and_puck_success Min 0 +[id0] expl/Final hand_and_puck_success Mean 0 +[id0] expl/Final hand_and_puck_success Std 0 +[id0] expl/Final hand_and_puck_success Max 0 +[id0] expl/Final hand_and_puck_success Min 0 +[id0] expl/hand_and_puck_success_2 Mean 0 +[id0] expl/hand_and_puck_success_2 Std 0 +[id0] expl/hand_and_puck_success_2 Max 0 +[id0] expl/hand_and_puck_success_2 Min 0 +[id0] expl/Final hand_and_puck_success_2 Mean 0 +[id0] expl/Final hand_and_puck_success_2 Std 0 +[id0] expl/Final hand_and_puck_success_2 Max 0 +[id0] expl/Final hand_and_puck_success_2 Min 0 +[id0] expl/hand_and_puck_success_3 Mean 0 +[id0] expl/hand_and_puck_success_3 Std 0 +[id0] expl/hand_and_puck_success_3 Max 0 +[id0] expl/hand_and_puck_success_3 Min 0 +[id0] expl/Final hand_and_puck_success_3 Mean 0 +[id0] expl/Final hand_and_puck_success_3 Std 0 +[id0] expl/Final hand_and_puck_success_3 Max 0 +[id0] expl/Final hand_and_puck_success_3 Min 0 +[id0] expl/state_success Mean 0 +[id0] expl/state_success Std 0 +[id0] expl/state_success Max 0 +[id0] expl/state_success Min 0 +[id0] expl/Final state_success Mean 0 +[id0] expl/Final state_success Std 0 +[id0] expl/Final state_success Max 0 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eval/Actions Mean -0.000333342 +[id0] eval/Actions Std 0.0505545 +[id0] eval/Actions Max 0.0510213 +[id0] eval/Actions Min -0.0517202 +[id0] eval/Num Paths 2 +[id0] eval/Average Returns -13.6151 +[id0] eval/hand_distance Mean 0.122697 +[id0] eval/hand_distance Std 0.0300833 +[id0] eval/hand_distance Max 0.180573 +[id0] eval/hand_distance Min 0.0919758 +[id0] eval/Final hand_distance Mean 0.140525 +[id0] eval/Final hand_distance Std 0.0400476 +[id0] eval/Final hand_distance Max 0.180573 +[id0] eval/Final hand_distance Min 0.100478 +[id0] eval/puck_distance Mean 0.272303 +[id0] eval/puck_distance Std 0.0149697 +[id0] eval/puck_distance Max 0.29101 +[id0] eval/puck_distance Min 0.25069 +[id0] eval/Final puck_distance Mean 0.288114 +[id0] eval/Final puck_distance Std 0.00289617 +[id0] eval/Final puck_distance Max 0.29101 +[id0] eval/Final puck_distance Min 0.285218 +[id0] eval/state_distance Mean 0.300344 +[id0] eval/state_distance Std 0.0112235 +[id0] eval/state_distance Max 0.342481 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hand_success_2 Mean 0 +[id0] eval/Final hand_success_2 Std 0 +[id0] eval/Final hand_success_2 Max 0 +[id0] eval/Final hand_success_2 Min 0 +[id0] eval/hand_success_3 Mean 0.5 +[id0] eval/hand_success_3 Std 0.5 +[id0] eval/hand_success_3 Max 1 +[id0] eval/hand_success_3 Min 0 +[id0] eval/Final hand_success_3 Mean 0.5 +[id0] eval/Final hand_success_3 Std 0.5 +[id0] eval/Final hand_success_3 Max 1 +[id0] eval/Final hand_success_3 Min 0 +[id0] eval/puck_success Mean 0 +[id0] eval/puck_success Std 0 +[id0] eval/puck_success Max 0 +[id0] eval/puck_success Min 0 +[id0] eval/Final puck_success Mean 0 +[id0] eval/Final puck_success Std 0 +[id0] eval/Final puck_success Max 0 +[id0] eval/Final puck_success Min 0 +[id0] eval/puck_success_2 Mean 0 +[id0] eval/puck_success_2 Std 0 +[id0] eval/puck_success_2 Max 0 +[id0] eval/puck_success_2 Min 0 +[id0] eval/Final puck_success_2 Mean 0 +[id0] eval/Final puck_success_2 Std 0 +[id0] eval/Final puck_success_2 Max 0 +[id0] eval/Final puck_success_2 Min 0 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eval/state_success_3 Std 0 +[id0] eval/state_success_3 Max 0 +[id0] eval/state_success_3 Min 0 +[id0] eval/Final state_success_3 Mean 0 +[id0] eval/Final state_success_3 Std 0 +[id0] eval/Final state_success_3 Max 0 +[id0] eval/Final state_success_3 Min 0 +[id0] eval/touch_success Mean 0 +[id0] eval/touch_success Std 0 +[id0] eval/touch_success Max 0 +[id0] eval/touch_success Min 0 +[id0] eval/Final touch_success Mean 0 +[id0] eval/Final touch_success Std 0 +[id0] eval/Final touch_success Max 0 +[id0] eval/Final touch_success Min 0 +[id0] eval/touch_success_2 Mean 0.45 +[id0] eval/touch_success_2 Std 0.497494 +[id0] eval/touch_success_2 Max 1 +[id0] eval/touch_success_2 Min 0 +[id0] eval/Final touch_success_2 Mean 0.5 +[id0] eval/Final touch_success_2 Std 0.5 +[id0] eval/Final touch_success_2 Max 1 +[id0] eval/Final touch_success_2 Min 0 +[id0] eval/touch_success_3 Mean 0.5 +[id0] eval/touch_success_3 Std 0.5 +[id0] eval/touch_success_3 Max 1 +[id0] eval/touch_success_3 Min 0 +[id0] eval/Final touch_success_3 Mean 0.5 +[id0] eval/Final touch_success_3 Std 0.5 +[id0] eval/Final touch_success_3 Max 1 +[id0] eval/Final touch_success_3 Min 0 +[id0] time/epoch_time (s) 3.64607 +[id0] time/evaluation sampling (s) 1.70982 +[id0] time/exploration sampling (s) 1.67064 +[id0] time/global_time (s) 26.3272 +[id0] time/replay buffer data storing (s) 0.000399828 +[id0] time/saving (s) 3.33786e-05 +[id0] time/training (s) 0.254038 +[id0] epoch 1 +[id0] --------------------------------------- --------------- +[id0] --------------------------------------- --------------- +[id0] trainer/num train calls 30 +[id0] trainer/QF1 Loss 0.00454581 +[id0] trainer/QF2 Loss 0.004166 +[id0] trainer/Policy Loss 2.72957 +[id0] trainer/Q1 Predictions Mean -0.126527 +[id0] trainer/Q1 Predictions Std 0.00315079 +[id0] trainer/Q1 Predictions Max -0.123503 +[id0] trainer/Q1 Predictions Min -0.131618 +[id0] trainer/Q2 Predictions Mean -0.126105 +[id0] trainer/Q2 Predictions Std 0.00710658 +[id0] trainer/Q2 Predictions Max -0.11948 +[id0] trainer/Q2 Predictions Min -0.139396 +[id0] trainer/Q Targets Mean -0.112228 +[id0] trainer/Q Targets Std 0.0672478 +[id0] trainer/Q Targets Max -0.0255882 +[id0] trainer/Q Targets Min -0.209882 +[id0] trainer/Log Pis Mean 2.6193 +[id0] trainer/Log Pis Std 1.044 +[id0] trainer/Log Pis Max 3.409 +[id0] trainer/Log Pis Min 0.627569 +[id0] trainer/rewards Mean -0.110253 +[id0] trainer/rewards Std 0.0672446 +[id0] trainer/rewards Max -0.0236798 +[id0] trainer/rewards Min -0.207899 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 90400 +[id0] trainer/policy/mean Mean 0.00452171 +[id0] trainer/policy/mean Std 0.199858 +[id0] trainer/policy/mean Max 0.211937 +[id0] trainer/policy/mean Min -0.202561 +[id0] trainer/policy/std Mean 0.0727627 +[id0] trainer/policy/std Std 0.000733147 +[id0] trainer/policy/std Max 0.0741526 +[id0] trainer/policy/std Min 0.0716186 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.303689 +[id0] trainer/Advantage Weights Max 0.803173 +[id0] trainer/Advantage Weights Min 0.000376373 +[id0] trainer/Advantage Score Mean -0.0162524 +[id0] trainer/Advantage Score Std 0.00252286 +[id0] trainer/Advantage Score Max -0.0131438 +[id0] trainer/Advantage Score Min -0.0208096 +[id0] trainer/bc/Train Logprob Loss 137.941 +[id0] trainer/bc/Test Logprob Loss 114.999 +[id0] trainer/bc/Train MSE 0.72753 +[id0] trainer/bc/Test MSE 0.686215 +[id0] trainer/bc/train_policy_loss 137.941 +[id0] trainer/bc/test_policy_loss 114.999 +[id0] expl/num steps total 400 +[id0] expl/num paths total 8 +[id0] expl/path length Mean 50 +[id0] expl/path length Std 0 +[id0] expl/path length Max 50 +[id0] expl/path length Min 50 +[id0] expl/Rewards Mean -0.145173 +[id0] expl/Rewards Std 0.0133283 +[id0] expl/Rewards Max -0.131845 +[id0] expl/Rewards Min -0.158502 +[id0] expl/Returns Mean -7.25866 +[id0] expl/Returns Std 0.666417 +[id0] expl/Returns Max -6.59225 +[id0] expl/Returns Min -7.92508 +[id0] expl/Actions Mean 0.0140931 +[id0] expl/Actions Std 0.209096 +[id0] expl/Actions Max 0.371834 +[id0] expl/Actions Min -0.3301 +[id0] expl/Num Paths 2 +[id0] expl/Average Returns -7.25866 +[id0] expl/hand_distance Mean 0.202981 +[id0] expl/hand_distance Std 0.0395357 +[id0] expl/hand_distance Max 0.271631 +[id0] expl/hand_distance Min 0.118211 +[id0] expl/Final hand_distance Mean 0.265121 +[id0] expl/Final hand_distance Std 0.00650981 +[id0] expl/Final hand_distance Max 0.271631 +[id0] expl/Final hand_distance Min 0.258611 +[id0] expl/puck_distance Mean 0.145173 +[id0] expl/puck_distance Std 0.0133283 +[id0] expl/puck_distance Max 0.158502 +[id0] expl/puck_distance Min 0.131845 +[id0] expl/Final puck_distance Mean 0.145173 +[id0] expl/Final puck_distance Std 0.0133284 +[id0] expl/Final puck_distance Max 0.158502 +[id0] expl/Final puck_distance Min 0.131845 +[id0] expl/state_distance Mean 0.250709 +[id0] expl/state_distance Std 0.0340966 +[id0] expl/state_distance Max 0.303319 +[id0] expl/state_distance Min 0.177079 +[id0] expl/Final state_distance Mean 0.302628 +[id0] expl/Final state_distance Std 0.000690746 +[id0] expl/Final state_distance Max 0.303319 +[id0] expl/Final state_distance Min 0.301938 +[id0] expl/touch_distance Mean 0.202697 +[id0] expl/touch_distance Std 0.0637517 +[id0] expl/touch_distance Max 0.312716 +[id0] expl/touch_distance Min 0.0831949 +[id0] expl/Final touch_distance Mean 0.197956 +[id0] expl/Final touch_distance Std 0.114761 +[id0] expl/Final touch_distance Max 0.312716 +[id0] expl/Final touch_distance Min 0.0831949 +[id0] expl/hand_success Mean 0 +[id0] expl/hand_success Std 0 +[id0] expl/hand_success Max 0 +[id0] expl/hand_success Min 0 +[id0] expl/Final hand_success Mean 0 +[id0] expl/Final hand_success Std 0 +[id0] expl/Final hand_success Max 0 +[id0] expl/Final hand_success Min 0 +[id0] expl/hand_success_2 Mean 0 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Mean 0 +[id0] expl/Final puck_success_2 Std 0 +[id0] expl/Final puck_success_2 Max 0 +[id0] expl/Final puck_success_2 Min 0 +[id0] expl/puck_success_3 Mean 0 +[id0] expl/puck_success_3 Std 0 +[id0] expl/puck_success_3 Max 0 +[id0] expl/puck_success_3 Min 0 +[id0] expl/Final puck_success_3 Mean 0 +[id0] expl/Final puck_success_3 Std 0 +[id0] expl/Final puck_success_3 Max 0 +[id0] expl/Final puck_success_3 Min 0 +[id0] expl/hand_and_puck_success Mean 0 +[id0] expl/hand_and_puck_success Std 0 +[id0] expl/hand_and_puck_success Max 0 +[id0] expl/hand_and_puck_success Min 0 +[id0] expl/Final hand_and_puck_success Mean 0 +[id0] expl/Final hand_and_puck_success Std 0 +[id0] expl/Final hand_and_puck_success Max 0 +[id0] expl/Final hand_and_puck_success Min 0 +[id0] expl/hand_and_puck_success_2 Mean 0 +[id0] expl/hand_and_puck_success_2 Std 0 +[id0] expl/hand_and_puck_success_2 Max 0 +[id0] expl/hand_and_puck_success_2 Min 0 +[id0] expl/Final hand_and_puck_success_2 Mean 0 +[id0] expl/Final hand_and_puck_success_2 Std 0 +[id0] expl/Final hand_and_puck_success_2 Max 0 +[id0] expl/Final hand_and_puck_success_2 Min 0 +[id0] expl/hand_and_puck_success_3 Mean 0 +[id0] expl/hand_and_puck_success_3 Std 0 +[id0] expl/hand_and_puck_success_3 Max 0 +[id0] expl/hand_and_puck_success_3 Min 0 +[id0] expl/Final hand_and_puck_success_3 Mean 0 +[id0] expl/Final hand_and_puck_success_3 Std 0 +[id0] expl/Final hand_and_puck_success_3 Max 0 +[id0] expl/Final hand_and_puck_success_3 Min 0 +[id0] expl/state_success Mean 0 +[id0] expl/state_success Std 0 +[id0] expl/state_success Max 0 +[id0] expl/state_success Min 0 +[id0] expl/Final state_success Mean 0 +[id0] expl/Final state_success Std 0 +[id0] expl/Final state_success Max 0 +[id0] expl/Final state_success Min 0 +[id0] expl/state_success_2 Mean 0 +[id0] expl/state_success_2 Std 0 +[id0] expl/state_success_2 Max 0 +[id0] expl/state_success_2 Min 0 +[id0] expl/Final state_success_2 Mean 0 +[id0] expl/Final state_success_2 Std 0 +[id0] expl/Final state_success_2 Max 0 +[id0] expl/Final state_success_2 Min 0 +[id0] expl/state_success_3 Mean 0 +[id0] expl/state_success_3 Std 0 +[id0] expl/state_success_3 Max 0 +[id0] expl/state_success_3 Min 0 +[id0] expl/Final state_success_3 Mean 0 +[id0] expl/Final state_success_3 Std 0 +[id0] expl/Final state_success_3 Max 0 +[id0] expl/Final state_success_3 Min 0 +[id0] expl/touch_success Mean 0 +[id0] expl/touch_success Std 0 +[id0] expl/touch_success Max 0 +[id0] expl/touch_success Min 0 +[id0] expl/Final touch_success Mean 0 +[id0] expl/Final touch_success Std 0 +[id0] expl/Final touch_success Max 0 +[id0] expl/Final touch_success Min 0 +[id0] expl/touch_success_2 Mean 0 +[id0] expl/touch_success_2 Std 0 +[id0] expl/touch_success_2 Max 0 +[id0] expl/touch_success_2 Min 0 +[id0] expl/Final touch_success_2 Mean 0 +[id0] expl/Final touch_success_2 Std 0 +[id0] expl/Final touch_success_2 Max 0 +[id0] expl/Final touch_success_2 Min 0 +[id0] expl/touch_success_3 Mean 0.14 +[id0] expl/touch_success_3 Std 0.346987 +[id0] expl/touch_success_3 Max 1 +[id0] expl/touch_success_3 Min 0 +[id0] expl/Final touch_success_3 Mean 0.5 +[id0] expl/Final touch_success_3 Std 0.5 +[id0] expl/Final touch_success_3 Max 1 +[id0] expl/Final touch_success_3 Min 0 +[id0] eval/num steps total 300 +[id0] eval/num paths total 6 +[id0] eval/path length Mean 50 +[id0] eval/path length Std 0 +[id0] eval/path length Max 50 +[id0] eval/path length Min 50 +[id0] eval/Rewards Mean -0.160745 +[id0] eval/Rewards Std 0.0629678 +[id0] eval/Rewards Max -0.0980016 +[id0] eval/Rewards Min -0.248806 +[id0] eval/Returns Mean -8.03726 +[id0] eval/Returns Std 3.13716 +[id0] eval/Returns Max -4.90009 +[id0] eval/Returns Min -11.1744 +[id0] eval/Actions Mean 0.00448906 +[id0] eval/Actions Std 0.198369 +[id0] eval/Actions Max 0.213171 +[id0] eval/Actions Min -0.203783 +[id0] eval/Num Paths 2 +[id0] eval/Average Returns -8.03726 +[id0] eval/hand_distance Mean 0.172838 +[id0] eval/hand_distance Std 0.0793458 +[id0] eval/hand_distance Max 0.338509 +[id0] eval/hand_distance Min 0.100819 +[id0] eval/Final hand_distance Mean 0.241763 +[id0] eval/Final hand_distance Std 0.0967464 +[id0] eval/Final hand_distance Max 0.338509 +[id0] eval/Final hand_distance Min 0.145016 +[id0] eval/puck_distance Mean 0.160745 +[id0] eval/puck_distance Std 0.0629678 +[id0] eval/puck_distance Max 0.248806 +[id0] eval/puck_distance Min 0.0980016 +[id0] eval/Final puck_distance Mean 0.173404 +[id0] eval/Final puck_distance Std 0.0754021 +[id0] eval/Final puck_distance Max 0.248806 +[id0] eval/Final puck_distance Min 0.0980023 +[id0] eval/state_distance Mean 0.252767 +[id0] eval/state_distance Std 0.0456258 +[id0] eval/state_distance Max 0.35241 +[id0] eval/state_distance Min 0.149458 +[id0] eval/Final state_distance Mean 0.320197 +[id0] eval/Final state_distance Std 0.0322134 +[id0] eval/Final state_distance Max 0.35241 +[id0] eval/Final state_distance Min 0.287983 +[id0] eval/touch_distance Mean 0.133625 +[id0] eval/touch_distance Std 0.0489162 +[id0] eval/touch_distance Max 0.22003 +[id0] eval/touch_distance Min 0.0647734 +[id0] eval/Final touch_distance Mean 0.143407 +[id0] eval/Final touch_distance Std 0.0766229 +[id0] eval/Final touch_distance Max 0.22003 +[id0] eval/Final touch_distance Min 0.0667838 +[id0] eval/hand_success Mean 0 +[id0] eval/hand_success Std 0 +[id0] eval/hand_success Max 0 +[id0] eval/hand_success Min 0 +[id0] eval/Final hand_success Mean 0 +[id0] eval/Final hand_success Std 0 +[id0] eval/Final hand_success Max 0 +[id0] eval/Final hand_success Min 0 +[id0] eval/hand_success_2 Mean 0 +[id0] eval/hand_success_2 Std 0 +[id0] eval/hand_success_2 Max 0 +[id0] eval/hand_success_2 Min 0 +[id0] eval/Final hand_success_2 Mean 0 +[id0] eval/Final hand_success_2 Std 0 +[id0] eval/Final hand_success_2 Max 0 +[id0] eval/Final hand_success_2 Min 0 +[id0] eval/hand_success_3 Mean 0.42 +[id0] eval/hand_success_3 Std 0.493559 +[id0] eval/hand_success_3 Max 1 +[id0] eval/hand_success_3 Min 0 +[id0] eval/Final hand_success_3 Mean 0 +[id0] eval/Final hand_success_3 Std 0 +[id0] eval/Final hand_success_3 Max 0 +[id0] eval/Final hand_success_3 Min 0 +[id0] eval/puck_success Mean 0 +[id0] eval/puck_success Std 0 +[id0] eval/puck_success Max 0 +[id0] eval/puck_success Min 0 +[id0] eval/Final puck_success Mean 0 +[id0] eval/Final puck_success Std 0 +[id0] eval/Final puck_success Max 0 +[id0] eval/Final puck_success Min 0 +[id0] eval/puck_success_2 Mean 0 +[id0] eval/puck_success_2 Std 0 +[id0] eval/puck_success_2 Max 0 +[id0] eval/puck_success_2 Min 0 +[id0] eval/Final puck_success_2 Mean 0 +[id0] eval/Final puck_success_2 Std 0 +[id0] eval/Final puck_success_2 Max 0 +[id0] eval/Final puck_success_2 Min 0 +[id0] eval/puck_success_3 Mean 0.5 +[id0] eval/puck_success_3 Std 0.5 +[id0] eval/puck_success_3 Max 1 +[id0] eval/puck_success_3 Min 0 +[id0] eval/Final puck_success_3 Mean 0.5 +[id0] eval/Final puck_success_3 Std 0.5 +[id0] eval/Final puck_success_3 Max 1 +[id0] eval/Final puck_success_3 Min 0 +[id0] eval/hand_and_puck_success Mean 0 +[id0] eval/hand_and_puck_success Std 0 +[id0] eval/hand_and_puck_success Max 0 +[id0] eval/hand_and_puck_success Min 0 +[id0] eval/Final hand_and_puck_success Mean 0 +[id0] eval/Final hand_and_puck_success Std 0 +[id0] eval/Final hand_and_puck_success Max 0 +[id0] eval/Final hand_and_puck_success Min 0 +[id0] eval/hand_and_puck_success_2 Mean 0 +[id0] eval/hand_and_puck_success_2 Std 0 +[id0] eval/hand_and_puck_success_2 Max 0 +[id0] eval/hand_and_puck_success_2 Min 0 +[id0] eval/Final hand_and_puck_success_2 Mean 0 +[id0] eval/Final hand_and_puck_success_2 Std 0 +[id0] eval/Final hand_and_puck_success_2 Max 0 +[id0] eval/Final hand_and_puck_success_2 Min 0 +[id0] eval/hand_and_puck_success_3 Mean 0 +[id0] eval/hand_and_puck_success_3 Std 0 +[id0] eval/hand_and_puck_success_3 Max 0 +[id0] eval/hand_and_puck_success_3 Min 0 +[id0] eval/Final hand_and_puck_success_3 Mean 0 +[id0] eval/Final hand_and_puck_success_3 Std 0 +[id0] eval/Final hand_and_puck_success_3 Max 0 +[id0] eval/Final hand_and_puck_success_3 Min 0 +[id0] eval/state_success Mean 0 +[id0] eval/state_success Std 0 +[id0] eval/state_success Max 0 +[id0] eval/state_success Min 0 +[id0] eval/Final state_success Mean 0 +[id0] eval/Final state_success Std 0 +[id0] eval/Final state_success Max 0 +[id0] eval/Final state_success Min 0 +[id0] eval/state_success_2 Mean 0 +[id0] eval/state_success_2 Std 0 +[id0] eval/state_success_2 Max 0 +[id0] eval/state_success_2 Min 0 +[id0] eval/Final state_success_2 Mean 0 +[id0] eval/Final state_success_2 Std 0 +[id0] eval/Final state_success_2 Max 0 +[id0] eval/Final state_success_2 Min 0 +[id0] eval/state_success_3 Mean 0 +[id0] eval/state_success_3 Std 0 +[id0] eval/state_success_3 Max 0 +[id0] eval/state_success_3 Min 0 +[id0] eval/Final state_success_3 Mean 0 +[id0] eval/Final state_success_3 Std 0 +[id0] eval/Final state_success_3 Max 0 +[id0] eval/Final state_success_3 Min 0 +[id0] eval/touch_success Mean 0 +[id0] eval/touch_success Std 0 +[id0] eval/touch_success Max 0 +[id0] eval/touch_success Min 0 +[id0] eval/Final touch_success Mean 0 +[id0] eval/Final touch_success Std 0 +[id0] eval/Final touch_success Max 0 +[id0] eval/Final touch_success Min 0 +[id0] eval/touch_success_2 Mean 0.16 +[id0] eval/touch_success_2 Std 0.366606 +[id0] eval/touch_success_2 Max 1 +[id0] eval/touch_success_2 Min 0 +[id0] eval/Final touch_success_2 Mean 0.5 +[id0] eval/Final touch_success_2 Std 0.5 +[id0] eval/Final touch_success_2 Max 1 +[id0] eval/Final touch_success_2 Min 0 +[id0] eval/touch_success_3 Mean 0.46 +[id0] eval/touch_success_3 Std 0.498397 +[id0] eval/touch_success_3 Max 1 +[id0] eval/touch_success_3 Min 0 +[id0] eval/Final touch_success_3 Mean 0.5 +[id0] eval/Final touch_success_3 Std 0.5 +[id0] eval/Final touch_success_3 Max 1 +[id0] eval/Final touch_success_3 Min 0 +[id0] time/epoch_time (s) 3.64969 +[id0] time/evaluation sampling (s) 1.69573 +[id0] time/exploration sampling (s) 1.6845 +[id0] time/global_time (s) 30.004 +[id0] time/replay buffer data storing (s) 0.000379324 +[id0] time/saving (s) 3.48091e-05 +[id0] time/training (s) 0.255474 +[id0] epoch 2 +[id0] --------------------------------------- --------------- +[id0] --------------------------------------- --------------- +[id0] trainer/num train calls 40 +[id0] trainer/QF1 Loss 0.00812755 +[id0] trainer/QF2 Loss 0.00844011 +[id0] trainer/Policy Loss 1.44796 +[id0] trainer/Q1 Predictions Mean -0.125104 +[id0] trainer/Q1 Predictions Std 0.00825019 +[id0] trainer/Q1 Predictions Max -0.115534 +[id0] trainer/Q1 Predictions Min -0.140015 +[id0] trainer/Q2 Predictions Mean -0.126615 +[id0] trainer/Q2 Predictions Std 0.00817811 +[id0] trainer/Q2 Predictions Max -0.115739 +[id0] trainer/Q2 Predictions Min -0.141015 +[id0] trainer/Q Targets Mean -0.0874891 +[id0] trainer/Q Targets Std 0.0767401 +[id0] trainer/Q Targets Max -0.00298979 +[id0] trainer/Q Targets Min -0.219601 +[id0] trainer/Log Pis Mean 1.32672 +[id0] trainer/Log Pis Std 0.705888 +[id0] trainer/Log Pis Max 1.97718 +[id0] trainer/Log Pis Min 0.302602 +[id0] trainer/rewards Mean -0.0843445 +[id0] trainer/rewards Std 0.0768233 +[id0] trainer/rewards Max -1.76237e-06 +[id0] trainer/rewards Min -0.216733 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 90500 +[id0] trainer/policy/mean Mean -0.0363329 +[id0] trainer/policy/mean Std 0.381041 +[id0] trainer/policy/mean Max 0.369571 +[id0] trainer/policy/mean Min -0.446347 +[id0] trainer/policy/std Mean 0.140297 +[id0] trainer/policy/std Std 0.0092635 +[id0] trainer/policy/std Max 0.156963 +[id0] trainer/policy/std Min 0.123789 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.399073 +[id0] trainer/Advantage Weights Max 0.998145 +[id0] trainer/Advantage Weights Min 3.91133e-10 +[id0] trainer/Advantage Score Mean -0.00386002 +[id0] trainer/Advantage Score Std 0.00749892 +[id0] trainer/Advantage Score Max 0.0071685 +[id0] trainer/Advantage Score Min -0.0144916 +[id0] trainer/bc/Train Logprob Loss 35.3732 +[id0] trainer/bc/Test Logprob Loss 31.4472 +[id0] trainer/bc/Train MSE 0.762608 +[id0] trainer/bc/Test MSE 0.794041 +[id0] trainer/bc/train_policy_loss 35.3732 +[id0] trainer/bc/test_policy_loss 31.4472 +[id0] expl/num steps total 500 +[id0] expl/num paths total 10 +[id0] expl/path length Mean 50 +[id0] expl/path length Std 0 +[id0] expl/path length Max 50 +[id0] expl/path length Min 50 +[id0] expl/Rewards Mean -0.113284 +[id0] expl/Rewards Std 0.00896539 +[id0] expl/Rewards Max -0.104318 +[id0] expl/Rewards Min -0.122249 +[id0] expl/Returns Mean -5.66419 +[id0] expl/Returns Std 0.448269 +[id0] expl/Returns Max -5.21592 +[id0] expl/Returns Min -6.11246 +[id0] expl/Actions Mean -0.0486082 +[id0] expl/Actions Std 0.417547 +[id0] expl/Actions Max 0.674142 +[id0] expl/Actions Min -0.728117 +[id0] expl/Num Paths 2 +[id0] expl/Average Returns -5.66419 +[id0] expl/hand_distance Mean 0.150248 +[id0] expl/hand_distance Std 0.0805957 +[id0] expl/hand_distance Max 0.233022 +[id0] expl/hand_distance Min 0.0281943 +[id0] expl/Final hand_distance Mean 0.162135 +[id0] expl/Final hand_distance Std 0.0708874 +[id0] expl/Final hand_distance Max 0.233022 +[id0] expl/Final hand_distance Min 0.0912476 +[id0] expl/puck_distance Mean 0.113284 +[id0] expl/puck_distance Std 0.00896539 +[id0] expl/puck_distance Max 0.122249 +[id0] expl/puck_distance Min 0.104318 +[id0] expl/Final puck_distance Mean 0.113284 +[id0] expl/Final puck_distance Std 0.00896559 +[id0] expl/Final puck_distance Max 0.122249 +[id0] expl/Final puck_distance Min 0.104318 +[id0] expl/state_distance Mean 0.193878 +[id0] expl/state_distance Std 0.0662949 +[id0] expl/state_distance Max 0.263143 +[id0] expl/state_distance Min 0.108062 +[id0] expl/Final state_distance Mean 0.200869 +[id0] expl/Final state_distance Std 0.0622744 +[id0] expl/Final state_distance Max 0.263143 +[id0] expl/Final state_distance Min 0.138594 +[id0] expl/touch_distance Mean 0.320496 +[id0] expl/touch_distance Std 0.0821977 +[id0] expl/touch_distance Max 0.413936 +[id0] expl/touch_distance Min 0.172705 +[id0] expl/Final touch_distance Mean 0.330567 +[id0] expl/Final touch_distance Std 0.0833687 +[id0] expl/Final touch_distance Max 0.413936 +[id0] expl/Final touch_distance Min 0.247198 +[id0] expl/hand_success Mean 0.17 +[id0] expl/hand_success Std 0.375633 +[id0] expl/hand_success Max 1 +[id0] expl/hand_success Min 0 +[id0] expl/Final hand_success Mean 0 +[id0] expl/Final hand_success Std 0 +[id0] expl/Final hand_success Max 0 +[id0] expl/Final hand_success Min 0 +[id0] expl/hand_success_2 Mean 0.2 +[id0] expl/hand_success_2 Std 0.4 +[id0] expl/hand_success_2 Max 1 +[id0] expl/hand_success_2 Min 0 +[id0] expl/Final hand_success_2 Mean 0 +[id0] expl/Final hand_success_2 Std 0 +[id0] expl/Final hand_success_2 Max 0 +[id0] expl/Final hand_success_2 Min 0 +[id0] expl/hand_success_3 Mean 0.5 +[id0] expl/hand_success_3 Std 0.5 +[id0] expl/hand_success_3 Max 1 +[id0] expl/hand_success_3 Min 0 +[id0] expl/Final hand_success_3 Mean 0.5 +[id0] expl/Final hand_success_3 Std 0.5 +[id0] expl/Final hand_success_3 Max 1 +[id0] expl/Final hand_success_3 Min 0 +[id0] expl/puck_success Mean 0 +[id0] expl/puck_success Std 0 +[id0] expl/puck_success Max 0 +[id0] expl/puck_success Min 0 +[id0] expl/Final puck_success Mean 0 +[id0] expl/Final puck_success Std 0 +[id0] expl/Final puck_success Max 0 +[id0] expl/Final puck_success Min 0 +[id0] expl/puck_success_2 Mean 0 +[id0] expl/puck_success_2 Std 0 +[id0] expl/puck_success_2 Max 0 +[id0] expl/puck_success_2 Min 0 +[id0] expl/Final puck_success_2 Mean 0 +[id0] expl/Final puck_success_2 Std 0 +[id0] expl/Final puck_success_2 Max 0 +[id0] expl/Final puck_success_2 Min 0 +[id0] expl/puck_success_3 Mean 0.5 +[id0] expl/puck_success_3 Std 0.5 +[id0] expl/puck_success_3 Max 1 +[id0] expl/puck_success_3 Min 0 +[id0] expl/Final puck_success_3 Mean 0.5 +[id0] expl/Final puck_success_3 Std 0.5 +[id0] expl/Final puck_success_3 Max 1 +[id0] expl/Final puck_success_3 Min 0 +[id0] expl/hand_and_puck_success Mean 0 +[id0] expl/hand_and_puck_success Std 0 +[id0] expl/hand_and_puck_success Max 0 +[id0] expl/hand_and_puck_success Min 0 +[id0] expl/Final hand_and_puck_success Mean 0 +[id0] expl/Final hand_and_puck_success Std 0 +[id0] expl/Final hand_and_puck_success Max 0 +[id0] expl/Final hand_and_puck_success Min 0 +[id0] expl/hand_and_puck_success_2 Mean 0 +[id0] expl/hand_and_puck_success_2 Std 0 +[id0] expl/hand_and_puck_success_2 Max 0 +[id0] expl/hand_and_puck_success_2 Min 0 +[id0] expl/Final hand_and_puck_success_2 Mean 0 +[id0] expl/Final hand_and_puck_success_2 Std 0 +[id0] expl/Final hand_and_puck_success_2 Max 0 +[id0] expl/Final hand_and_puck_success_2 Min 0 +[id0] expl/hand_and_puck_success_3 Mean 0 +[id0] expl/hand_and_puck_success_3 Std 0 +[id0] expl/hand_and_puck_success_3 Max 0 +[id0] expl/hand_and_puck_success_3 Min 0 +[id0] expl/Final hand_and_puck_success_3 Mean 0 +[id0] expl/Final hand_and_puck_success_3 Std 0 +[id0] expl/Final hand_and_puck_success_3 Max 0 +[id0] expl/Final hand_and_puck_success_3 Min 0 +[id0] expl/state_success Mean 0 +[id0] expl/state_success Std 0 +[id0] expl/state_success Max 0 +[id0] expl/state_success Min 0 +[id0] expl/Final state_success Mean 0 +[id0] expl/Final state_success Std 0 +[id0] expl/Final state_success Max 0 +[id0] expl/Final state_success Min 0 +[id0] expl/state_success_2 Mean 0 +[id0] expl/state_success_2 Std 0 +[id0] expl/state_success_2 Max 0 +[id0] expl/state_success_2 Min 0 +[id0] expl/Final state_success_2 Mean 0 +[id0] expl/Final state_success_2 Std 0 +[id0] expl/Final state_success_2 Max 0 +[id0] expl/Final state_success_2 Min 0 +[id0] expl/state_success_3 Mean 0.17 +[id0] expl/state_success_3 Std 0.375633 +[id0] expl/state_success_3 Max 1 +[id0] expl/state_success_3 Min 0 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eval/Final hand_distance Min 0.141363 +[id0] eval/puck_distance Mean 0.127591 +[id0] eval/puck_distance Std 0.0872006 +[id0] eval/puck_distance Max 0.214791 +[id0] eval/puck_distance Min 0.0403897 +[id0] eval/Final puck_distance Mean 0.127591 +[id0] eval/Final puck_distance Std 0.0872005 +[id0] eval/Final puck_distance Max 0.214791 +[id0] eval/Final puck_distance Min 0.0403904 +[id0] eval/state_distance Mean 0.194302 +[id0] eval/state_distance Std 0.0759312 +[id0] eval/state_distance Max 0.269184 +[id0] eval/state_distance Min 0.0403941 +[id0] eval/Final state_distance Mean 0.223129 +[id0] eval/Final state_distance Std 0.0340064 +[id0] eval/Final state_distance Max 0.257136 +[id0] eval/Final state_distance Min 0.189123 +[id0] eval/touch_distance Mean 0.242917 +[id0] eval/touch_distance Std 0.0917106 +[id0] eval/touch_distance Max 0.337246 +[id0] eval/touch_distance Min 0.0830543 +[id0] eval/Final touch_distance Mean 0.275227 +[id0] eval/Final touch_distance Std 0.0620179 +[id0] eval/Final touch_distance Max 0.337245 +[id0] eval/Final touch_distance Min 0.213209 +[id0] eval/hand_success Mean 0.11 +[id0] eval/hand_success Std 0.31289 +[id0] eval/hand_success Max 1 +[id0] eval/hand_success Min 0 +[id0] eval/Final hand_success Mean 0 +[id0] eval/Final hand_success Std 0 +[id0] eval/Final hand_success Max 0 +[id0] eval/Final hand_success Min 0 +[id0] eval/hand_success_2 Mean 0.16 +[id0] eval/hand_success_2 Std 0.366606 +[id0] eval/hand_success_2 Max 1 +[id0] eval/hand_success_2 Min 0 +[id0] eval/Final hand_success_2 Mean 0 +[id0] eval/Final hand_success_2 Std 0 +[id0] eval/Final hand_success_2 Max 0 +[id0] eval/Final hand_success_2 Min 0 +[id0] eval/hand_success_3 Mean 0.24 +[id0] eval/hand_success_3 Std 0.427083 +[id0] eval/hand_success_3 Max 1 +[id0] eval/hand_success_3 Min 0 +[id0] eval/Final hand_success_3 Mean 0 +[id0] eval/Final hand_success_3 Std 0 +[id0] eval/Final hand_success_3 Max 0 +[id0] eval/Final hand_success_3 Min 0 +[id0] eval/puck_success Mean 0.5 +[id0] eval/puck_success Std 0.5 +[id0] eval/puck_success Max 1 +[id0] eval/puck_success Min 0 +[id0] eval/Final puck_success Mean 0.5 +[id0] eval/Final puck_success Std 0.5 +[id0] eval/Final puck_success Max 1 +[id0] eval/Final puck_success Min 0 +[id0] eval/puck_success_2 Mean 0.5 +[id0] eval/puck_success_2 Std 0.5 +[id0] eval/puck_success_2 Max 1 +[id0] eval/puck_success_2 Min 0 +[id0] eval/Final puck_success_2 Mean 0.5 +[id0] eval/Final puck_success_2 Std 0.5 +[id0] eval/Final puck_success_2 Max 1 +[id0] eval/Final puck_success_2 Min 0 +[id0] eval/puck_success_3 Mean 0.5 +[id0] eval/puck_success_3 Std 0.5 +[id0] eval/puck_success_3 Max 1 +[id0] eval/puck_success_3 Min 0 +[id0] eval/Final puck_success_3 Mean 0.5 +[id0] eval/Final puck_success_3 Std 0.5 +[id0] eval/Final puck_success_3 Max 1 +[id0] eval/Final puck_success_3 Min 0 +[id0] eval/hand_and_puck_success Mean 0.03 +[id0] eval/hand_and_puck_success Std 0.170587 +[id0] eval/hand_and_puck_success Max 1 +[id0] eval/hand_and_puck_success Min 0 +[id0] eval/Final hand_and_puck_success Mean 0 +[id0] eval/Final hand_and_puck_success Std 0 +[id0] eval/Final hand_and_puck_success Max 0 +[id0] eval/Final hand_and_puck_success Min 0 +[id0] eval/hand_and_puck_success_2 Mean 0.07 +[id0] eval/hand_and_puck_success_2 Std 0.255147 +[id0] eval/hand_and_puck_success_2 Max 1 +[id0] eval/hand_and_puck_success_2 Min 0 +[id0] eval/Final hand_and_puck_success_2 Mean 0 +[id0] eval/Final hand_and_puck_success_2 Std 0 +[id0] eval/Final hand_and_puck_success_2 Max 0 +[id0] eval/Final hand_and_puck_success_2 Min 0 +[id0] eval/hand_and_puck_success_3 Mean 0.16 +[id0] eval/hand_and_puck_success_3 Std 0.366606 +[id0] eval/hand_and_puck_success_3 Max 1 +[id0] eval/hand_and_puck_success_3 Min 0 +[id0] eval/Final hand_and_puck_success_3 Mean 0 +[id0] eval/Final hand_and_puck_success_3 Std 0 +[id0] eval/Final hand_and_puck_success_3 Max 0 +[id0] eval/Final hand_and_puck_success_3 Min 0 +[id0] eval/state_success Mean 0.08 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2.83718e-05 +[id0] time/training (s) 0.240659 +[id0] epoch 3 +[id0] --------------------------------------- --------------- +[id0] --------------------------------------- --------------- +[id0] trainer/num train calls 50 +[id0] trainer/QF1 Loss 0.0135741 +[id0] trainer/QF2 Loss 0.0132348 +[id0] trainer/Policy Loss -1.0125 +[id0] trainer/Q1 Predictions Mean -0.115927 +[id0] trainer/Q1 Predictions Std 0.015483 +[id0] trainer/Q1 Predictions Max -0.0978391 +[id0] trainer/Q1 Predictions Min -0.137299 +[id0] trainer/Q2 Predictions Mean -0.11971 +[id0] trainer/Q2 Predictions Std 0.0153614 +[id0] trainer/Q2 Predictions Max -0.099684 +[id0] trainer/Q2 Predictions Min -0.141439 +[id0] trainer/Q Targets Mean -0.124712 +[id0] trainer/Q Targets Std 0.111004 +[id0] trainer/Q Targets Max -0.00466002 +[id0] trainer/Q Targets Min -0.280714 +[id0] trainer/Log Pis Mean -1.13064 +[id0] trainer/Log Pis Std 1.98296 +[id0] trainer/Log Pis Max 0.0934451 +[id0] trainer/Log Pis Min -5.06595 +[id0] trainer/rewards Mean -0.120223 +[id0] trainer/rewards Std 0.111247 +[id0] trainer/rewards Max -8.42937e-08 +[id0] trainer/rewards Min -0.276419 +[id0] trainer/terminals Mean 0 +[id0] trainer/terminals Std 0 +[id0] trainer/terminals Max 0 +[id0] trainer/terminals Min 0 +[id0] trainer/replay_buffer_len 90600 +[id0] trainer/policy/mean Mean -0.0841174 +[id0] trainer/policy/mean Std 0.459796 +[id0] trainer/policy/mean Max 0.393837 +[id0] trainer/policy/mean Min -0.566948 +[id0] trainer/policy/std Mean 0.343178 +[id0] trainer/policy/std Std 0.0279913 +[id0] trainer/policy/std Max 0.393021 +[id0] trainer/policy/std Min 0.302896 +[id0] trainer/Advantage Weights Mean 0.2 +[id0] trainer/Advantage Weights Std 0.392735 +[id0] trainer/Advantage Weights Max 0.985392 +[id0] trainer/Advantage Weights Min 7.05268e-16 +[id0] trainer/Advantage Score Mean 0.00222292 +[id0] trainer/Advantage Score Std 0.0124918 +[id0] trainer/Advantage Score Max 0.0155132 +[id0] trainer/Advantage Score Min -0.0193601 +[id0] trainer/bc/Train Logprob Loss 6.82 +[id0] trainer/bc/Test Logprob Loss 5.87462 +[id0] trainer/bc/Train MSE 1.0784 +[id0] trainer/bc/Test MSE 1.02518 +[id0] trainer/bc/train_policy_loss 6.82 +[id0] trainer/bc/test_policy_loss 5.87462 +[id0] expl/num steps total 600 +[id0] expl/num paths total 12 +[id0] expl/path length Mean 50 +[id0] expl/path length Std 0 +[id0] expl/path length Max 50 +[id0] expl/path length Min 50 +[id0] expl/Rewards Mean -0.195256 +[id0] expl/Rewards Std 0.107581 +[id0] expl/Rewards Max -0.0480694 +[id0] expl/Rewards Min -0.301743 +[id0] expl/Returns Mean -9.76281 +[id0] expl/Returns Std 5.32436 +[id0] expl/Returns Max -4.43845 +[id0] expl/Returns Min -15.0872 +[id0] expl/Actions Mean -0.0733707 +[id0] expl/Actions Std 0.62436 +[id0] expl/Actions Max 1.42151 +[id0] expl/Actions Min -1.46905 +[id0] expl/Num Paths 2 +[id0] expl/Average Returns -9.76281 +[id0] expl/hand_distance Mean 0.158642 +[id0] expl/hand_distance Std 0.0608491 +[id0] expl/hand_distance Max 0.245079 +[id0] expl/hand_distance Min 0.0291545 +[id0] expl/Final hand_distance Mean 0.197413 +[id0] expl/Final hand_distance Std 0.0476639 +[id0] expl/Final hand_distance Max 0.245077 +[id0] expl/Final hand_distance Min 0.149749 +[id0] expl/puck_distance Mean 0.195256 +[id0] expl/puck_distance Std 0.107581 +[id0] expl/puck_distance Max 0.301743 +[id0] expl/puck_distance Min 0.0480694 +[id0] expl/Final puck_distance Mean 0.20168 +[id0] expl/Final puck_distance Std 0.100063 +[id0] expl/Final puck_distance Max 0.301743 +[id0] expl/Final puck_distance Min 0.101617 +[id0] expl/state_distance Mean 0.256775 +[id0] expl/state_distance Std 0.112407 +[id0] expl/state_distance Max 0.388732 +[id0] expl/state_distance Min 0.0562197 +[id0] expl/Final state_distance Mean 0.284851 +[id0] expl/Final state_distance Std 0.10388 +[id0] expl/Final state_distance Max 0.388731 +[id0] expl/Final state_distance Min 0.180972 +[id0] expl/touch_distance Mean 0.105648 +[id0] expl/touch_distance Std 0.0463963 +[id0] expl/touch_distance Max 0.261491 +[id0] expl/touch_distance Min 0.0616572 +[id0] expl/Final touch_distance Mean 0.0855933 +[id0] expl/Final touch_distance Std 0.00990345 +[id0] expl/Final touch_distance Max 0.0954967 +[id0] expl/Final touch_distance Min 0.0756899 +[id0] expl/hand_success Mean 0.06 +[id0] expl/hand_success Std 0.237487 +[id0] expl/hand_success Max 1 +[id0] expl/hand_success Min 0 +[id0] expl/Final hand_success Mean 0 +[id0] expl/Final hand_success Std 0 +[id0] expl/Final hand_success Max 0 +[id0] expl/Final hand_success Min 0 +[id0] expl/hand_success_2 Mean 0.11 +[id0] expl/hand_success_2 Std 0.31289 +[id0] expl/hand_success_2 Max 1 +[id0] expl/hand_success_2 Min 0 +[id0] expl/Final hand_success_2 Mean 0 +[id0] expl/Final hand_success_2 Std 0 +[id0] expl/Final hand_success_2 Max 0 +[id0] expl/Final hand_success_2 Min 0 +[id0] expl/hand_success_3 Mean 0.22 +[id0] expl/hand_success_3 Std 0.414246 +[id0] expl/hand_success_3 Max 1 +[id0] expl/hand_success_3 Min 0 +[id0] expl/Final hand_success_3 Mean 0 +[id0] expl/Final hand_success_3 Std 0 +[id0] expl/Final hand_success_3 Max 0 +[id0] expl/Final hand_success_3 Min 0 +[id0] expl/puck_success Mean 0.1 +[id0] expl/puck_success Std 0.3 +[id0] expl/puck_success Max 1 +[id0] expl/puck_success Min 0 +[id0] expl/Final puck_success Mean 0 +[id0] expl/Final puck_success Std 0 +[id0] expl/Final puck_success Max 0 +[id0] expl/Final puck_success Min 0 +[id0] expl/puck_success_2 Mean 0.12 +[id0] expl/puck_success_2 Std 0.324962 +[id0] expl/puck_success_2 Max 1 +[id0] expl/puck_success_2 Min 0 +[id0] expl/Final puck_success_2 Mean 0 +[id0] expl/Final puck_success_2 Std 0 +[id0] expl/Final puck_success_2 Max 0 +[id0] expl/Final puck_success_2 Min 0 +[id0] expl/puck_success_3 Mean 0.5 +[id0] expl/puck_success_3 Std 0.5 +[id0] expl/puck_success_3 Max 1 +[id0] expl/puck_success_3 Min 0 +[id0] expl/Final puck_success_3 Mean 0.5 +[id0] expl/Final puck_success_3 Std 0.5 +[id0] expl/Final puck_success_3 Max 1 +[id0] expl/Final puck_success_3 Min 0 +[id0] expl/hand_and_puck_success Mean 0 +[id0] expl/hand_and_puck_success Std 0 +[id0] expl/hand_and_puck_success Max 0 +[id0] expl/hand_and_puck_success Min 0 +[id0] expl/Final hand_and_puck_success Mean 0 +[id0] expl/Final hand_and_puck_success Std 0 +[id0] expl/Final hand_and_puck_success Max 0 +[id0] expl/Final hand_and_puck_success Min 0 +[id0] expl/hand_and_puck_success_2 Mean 0.03 +[id0] expl/hand_and_puck_success_2 Std 0.170587 +[id0] expl/hand_and_puck_success_2 Max 1 +[id0] expl/hand_and_puck_success_2 Min 0 +[id0] expl/Final hand_and_puck_success_2 Mean 0 +[id0] expl/Final hand_and_puck_success_2 Std 0 +[id0] expl/Final hand_and_puck_success_2 Max 0 +[id0] expl/Final hand_and_puck_success_2 Min 0 +[id0] expl/hand_and_puck_success_3 Mean 0.08 +[id0] expl/hand_and_puck_success_3 Std 0.271293 +[id0] expl/hand_and_puck_success_3 Max 1 +[id0] expl/hand_and_puck_success_3 Min 0 +[id0] expl/Final hand_and_puck_success_3 Mean 0 +[id0] expl/Final hand_and_puck_success_3 Std 0 +[id0] expl/Final hand_and_puck_success_3 Max 0 +[id0] expl/Final hand_and_puck_success_3 Min 0 +[id0] expl/state_success Mean 0.04 +[id0] expl/state_success Std 0.195959 +[id0] expl/state_success Max 1 +[id0] expl/state_success Min 0 +[id0] expl/Final state_success Mean 0 +[id0] expl/Final state_success Std 0 +[id0] expl/Final state_success Max 0 +[id0] expl/Final state_success Min 0 +[id0] expl/state_success_2 Mean 0.06 +[id0] expl/state_success_2 Std 0.237487 +[id0] expl/state_success_2 Max 1 +[id0] expl/state_success_2 Min 0 +[id0] expl/Final state_success_2 Mean 0 +[id0] expl/Final state_success_2 Std 0 +[id0] expl/Final state_success_2 Max 0 +[id0] expl/Final state_success_2 Min 0 +[id0] expl/state_success_3 Mean 0.13 +[id0] expl/state_success_3 Std 0.336303 +[id0] expl/state_success_3 Max 1 +[id0] expl/state_success_3 Min 0 +[id0] expl/Final state_success_3 Mean 0 +[id0] expl/Final state_success_3 Std 0 +[id0] expl/Final state_success_3 Max 0 +[id0] 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diff --git a/tests/regression/awac/gcrl/id0/script_name.txt b/tests/regression/awac/gcrl/id0/script_name.txt new file mode 100644 index 000000000..51cb795d4 --- /dev/null +++ b/tests/regression/awac/gcrl/id0/script_name.txt @@ -0,0 +1 @@ +experiments/ashvin/clearning/debug/pusher_awac5.py \ No newline at end of file diff --git a/tests/regression/awac/gcrl/id0/variant.json b/tests/regression/awac/gcrl/id0/variant.json new file mode 100644 index 000000000..2612a414c --- /dev/null +++ b/tests/regression/awac/gcrl/id0/variant.json @@ -0,0 +1,135 @@ +{ + "achieved_goal_key": "state_achieved_goal", + "add_env_demos": false, + "add_env_offpolicy_data": false, + "algorithm": "SAC", + "batch_size": 5, + "collection_mode": "batch", + "debug": true, + "demo_replay_buffer_kwargs": { + "fraction_goals_env_goals": 0.0, + "fraction_goals_rollout_goals": 1.0 + }, + "desired_goal_key": "state_desired_goal", + "env_class": { + "$class": 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a/tests/regression/awac/gcrl/id0_offline/pretrain_q.csv b/tests/regression/awac/gcrl/id0_offline/pretrain_q.csv new file mode 100644 index 000000000..527d4004c --- /dev/null +++ b/tests/regression/awac/gcrl/id0_offline/pretrain_q.csv @@ -0,0 +1,2 @@ +trainer/Advantage Score Max,trainer/Advantage Score Mean,trainer/Advantage Score Min,trainer/Advantage Score Std,trainer/Advantage Weights Max,trainer/Advantage Weights Mean,trainer/Advantage Weights Min,trainer/Advantage Weights Std,trainer/Log Pis Max,trainer/Log Pis Mean,trainer/Log Pis Min,trainer/Log Pis Std,trainer/Policy Loss,trainer/Q Targets Max,trainer/Q Targets Mean,trainer/Q Targets Min,trainer/Q Targets Std,trainer/Q1 Predictions Max,trainer/Q1 Predictions Mean,trainer/Q1 Predictions Min,trainer/Q1 Predictions Std,trainer/Q2 Predictions Max,trainer/Q2 Predictions Mean,trainer/Q2 Predictions Min,trainer/Q2 Predictions Std,trainer/QF1 Loss,trainer/QF2 Loss,trainer/batch,trainer/bc/Test Logprob Loss,trainer/bc/Test MSE,trainer/bc/Train Logprob Loss,trainer/bc/Train MSE,trainer/bc/test_policy_loss,trainer/bc/train_policy_loss,trainer/epoch_time,trainer/policy/mean Max,trainer/policy/mean Mean,trainer/policy/mean Min,trainer/policy/mean Std,trainer/policy/std Max,trainer/policy/std Mean,trainer/policy/std Min,trainer/policy/std Std,trainer/replay_buffer_len,trainer/rewards Max,trainer/rewards Mean,trainer/rewards Min,trainer/rewards Std,trainer/terminals Max,trainer/terminals Mean,trainer/terminals Min,trainer/terminals Std +0.00035101734,-0.00015639616,-0.00069705193,0.00028063633,0.012491897,0.007812501,0.004379834,0.0020998572,4.1562147,3.14975,-1.8516588,1.099994,3.150136,-0.011974041,-0.102385044,-0.39087182,0.10003365,-6.9917987e-06,-0.0005423832,-0.0010567228,0.00028634752,-3.3223332e-06,-0.00047541893,-0.0007876997,0.00018604635,0.020397784,0.020407204,0,260.2997,0.66357476,276.1372,0.6956859,260.2997,276.1372,0.02984142303466797,-3.0967305e-05,-4.0639505e-05,-5.2738804e-05,4.9147916e-06,0.049802598,0.049794674,0.049786747,7.0395695e-06,800,-0.012122636,-0.10243775,-0.39088753,0.09997146,0.0,0.0,0.0,0.0 diff --git a/tests/regression/awac/gcrl/id0_offline/progress.csv b/tests/regression/awac/gcrl/id0_offline/progress.csv new file mode 100644 index 000000000..e69de29bb diff --git a/tests/regression/awac/gcrl/id0_offline/script_name.txt b/tests/regression/awac/gcrl/id0_offline/script_name.txt new file mode 100644 index 000000000..68ea43f6b --- /dev/null +++ b/tests/regression/awac/gcrl/id0_offline/script_name.txt @@ -0,0 +1 @@ +tests/regression/awac/gcrl/test_awac_gcrl_offline.py \ No newline at end of file diff --git a/tests/regression/awac/gcrl/id0_offline/variant.json b/tests/regression/awac/gcrl/id0_offline/variant.json new file mode 100644 index 000000000..8cb84df30 --- /dev/null +++ b/tests/regression/awac/gcrl/id0_offline/variant.json @@ -0,0 +1,111 @@ +{ + "achieved_goal_key": "state_achieved_goal", + "add_env_demos": false, + "add_env_offpolicy_data": false, + "algorithm": "SAC", + "batch_size": 5, + "collection_mode": "batch", + "demo_replay_buffer_kwargs": { + "fraction_goals_env_goals": 0.0, + "fraction_goals_rollout_goals": 1.0, + "recompute_rewards": false + }, + "desired_goal_key": "state_desired_goal", + "env_class": { + "$class": "rlkit.testing.stub_classes.StubMultiEnv" + }, + "env_kwargs": { + "action_dim": 2, + "obs_dims": { + "state_achieved_goal": 4, + "state_desired_goal": 4, + "state_observation": 4 + }, + "reward_type": "puck_distance" + }, + "exp_id": 0, + "exp_name": "references/awac/gcrl/pusher_offline1", + "layer_size": 256, + "load_demos": true, + "logger_config": { + "run_id": 0, + "snapshot_gap": 100, + "snapshot_mode": "gap" + }, + "max_path_length": 200, + "min_num_steps_before_training": 1000, + "num_epochs": 0, + "num_eval_steps_per_epoch": 1000, + "num_expl_steps_per_train_loop": 1000, + "num_exps_per_instance": 1, + "num_trains_per_train_loop": 4000, + "observation_key": "state_observation", + "path_loader_class": { + "$class": "rlkit.demos.source.dict_to_mdp_path_loader.DictToMDPPathLoader" + }, + "path_loader_kwargs": { + "demo_paths": [ + { + "data_split": 1, + "is_demo": true, + "obs_dict": false, + "path": "gcrl_data_mini/id0/video_0_vae.p" + } + ] + }, + "policy_class": { + "$class": "rlkit.torch.sac.policies.gaussian_policy.GaussianPolicy" + }, + "policy_kwargs": { + "hidden_sizes": [ + 256, + 256, + 256, + 256 + ], + "max_log_std": 0, + "min_log_std": -6, + "std_architecture": "shared" + }, + "pretrain_policy": true, + "pretrain_rl": true, + "pretraining_logging_period": 1, + "region": "us-west-2", + "replay_buffer_kwargs": { + "fraction_goals_env_goals": 0.0, + "fraction_goals_rollout_goals": 0.2, + "max_size": 1000000, + "recompute_rewards": false + }, + "run_id": 0, + "seed": "0", + "seedid": 0, + "trainer_kwargs": { + "alpha": 0, + "awr_weight": 1.0, + "bc_num_pretrain_steps": 0, + "bc_weight": 0.0, + "beta": 0.001, + "compute_bc": true, + "discount": 0.99, + "policy_lr": 0.0003, + "policy_weight_decay": 0.0001, + "q_num_pretrain1_steps": 0, + "q_num_pretrain2_steps": 10, + "q_weight_decay": 0, + "qf_lr": 0.0003, + "reparam_weight": 0.0, + "reward_scale": 1, + "reward_transform_kwargs": null, + "rl_weight": 1.0, + "soft_target_tau": 0.005, + "target_update_period": 1, + "terminal_transform_kwargs": null, + "use_automatic_entropy_tuning": false, + "use_awr_update": true, + "use_reparam_update": false + }, + "trial_name": "id0", + "unique_id": "35082356-e289-43de-9f37-ca068cfb8be5", + "version": "normal" +} \ No newline at end of file diff --git a/tests/regression/awac/gcrl/test_awac_gcrl_offline.py b/tests/regression/awac/gcrl/test_awac_gcrl_offline.py new file mode 100644 index 000000000..6806b596a --- /dev/null +++ b/tests/regression/awac/gcrl/test_awac_gcrl_offline.py @@ -0,0 +1,179 @@ +"""Test algorithm without requiring env setup. Incomplete due to reliance on env.sample_goals""" + +from rlkit.core import logger +from rlkit.testing import csv_util + + +from rlkit.demos.source.dict_to_mdp_path_loader import DictToMDPPathLoader +from rlkit.launchers.experiments.awac.awac_gcrl import experiment, process_args + +import rlkit.util.hyperparameter as hyp +from rlkit.launchers.arglauncher import run_variants + +from rlkit.torch.sac.policies import GaussianPolicy + +from rlkit.testing.stub_classes import StubEnv, StubMultiEnv +# from multiworld.envs.mujoco.sawyer_xyz.sawyer_push_leap import SawyerPushAndReachXYEnv + +def main(): + variant = dict( + num_epochs=501, + num_eval_steps_per_epoch=1000, + num_trains_per_train_loop=4000, + num_expl_steps_per_train_loop=1000, + min_num_steps_before_training=1000, + max_path_length=200, + batch_size=1024, + + replay_buffer_kwargs=dict( + max_size=int(1E6), + fraction_goals_rollout_goals=0.2, + fraction_goals_env_goals=0.5, + ), + demo_replay_buffer_kwargs=dict( + fraction_goals_rollout_goals=1.0, + fraction_goals_env_goals=0.0, + ), + + layer_size=256, + policy_class=GaussianPolicy, + policy_kwargs=dict( + hidden_sizes=[256, 256, 256, 256], + max_log_std=0, + min_log_std=-4, + std_architecture="shared", + # num_gaussians=1, + ), + + algorithm="SAC", + version="normal", + collection_mode='batch', + trainer_kwargs=dict( + discount=0.99, + soft_target_tau=5e-3, + target_update_period=1, + policy_lr=3E-4, + qf_lr=3E-4, + reward_scale=1, + beta=1, + use_automatic_entropy_tuning=False, + alpha=0, + + bc_num_pretrain_steps=0, + q_num_pretrain1_steps=0, + q_num_pretrain2_steps=25000, + policy_weight_decay=1e-4, + q_weight_decay=0, + + rl_weight=1.0, + use_awr_update=True, + use_reparam_update=False, + compute_bc=True, + reparam_weight=0.0, + awr_weight=1.0, + bc_weight=0.0, + + reward_transform_kwargs=None, # r' = r + 1 + terminal_transform_kwargs=None, # t = 0 + ), + num_exps_per_instance=1, + region='us-west-2', + + path_loader_class=DictToMDPPathLoader, + path_loader_kwargs=dict( + demo_paths=[ + dict( + path="ashvin/icml2020/pusher/state2/random2/run12/id*/video_*_vae.p", + sync_dir="ashvin/icml2020/pusher/state2/random2/run12", + obs_dict=False, # misleading but this arg is really "unwrap_obs_dict" + is_demo=True, + data_split=0.1, + ), + # dict( + # path="demos/icml2020/hand/pen_bc5.npy", + # obs_dict=False, + # is_demo=False, + # train_split=0.9, + # ), + ], + ), + add_env_demos=False, + add_env_offpolicy_data=False, + + # logger_variant=dict( + # tensorboard=True, + # ), + load_demos=True, + pretrain_policy=True, + pretrain_rl=True, + # save_pretrained_algorithm=True, + # snapshot_mode="all", + env_class=StubMultiEnv, + env_kwargs=dict( + obs_dims=dict( + state_observation=4, + state_desired_goal=4, + state_achieved_goal=4, + ), + action_dim=2, + ), + + observation_key="state_observation", + desired_goal_key="state_desired_goal", + achieved_goal_key="state_achieved_goal", + ) + + + search_space = { + 'seedid': range(5), + 'trainer_kwargs.beta': [0.001, ], + 'num_trains_per_train_loop': [4000], + 'env_kwargs.reward_type': ['puck_distance', ], + 'policy_kwargs.min_log_std': [-6, ], + # 'trainer_kwargs.bc_weight': [0, 1], + + # env-specific hacks + 'replay_buffer_kwargs.fraction_goals_env_goals': [0.0], + 'replay_buffer_kwargs.recompute_rewards': [False], + 'demo_replay_buffer_kwargs.recompute_rewards': [False], + + # make experiment short and only offline + 'batch_size': [5], + 'num_epochs': [0], + 'pretraining_logging_period': [1], + 'trainer_kwargs.q_num_pretrain2_steps': [10], + 'path_loader_kwargs.demo_paths': [ + [dict( + path=os.getcwd() + "/tests/regression/awac/gcrl/gcrl_data_mini/id0/video_0_vae.p", + obs_dict=False, # misleading but this arg is really "unwrap_obs_dict" + is_demo=True, + data_split=1, + ),], + ], + } + + sweeper = hyp.DeterministicHyperparameterSweeper( + search_space, default_parameters=variant, + ) + + variants = [] + for variant in sweeper.iterate_hyperparameters(): + variants.append(variant) + + run_variants(experiment, variants, process_args) + +import os +import sys +def test_awac_gcrl_online(): + cmd = "python experiments/references/awac/gcrl/pusher_offline1.py --1 --local --gpu --run 0 --seed 0" + sys.argv = cmd.split(" ")[1:] + main() + reference_csv = "tests/regression/awac/gcrl/id0_offline/pretrain_q.csv" + output_csv = os.path.join(logger.get_snapshot_dir(), "pretrain_q.csv") + output = csv_util.get_exp(output_csv) + reference = csv_util.get_exp(reference_csv) + keys = ["trainer/batch", "trainer/Advantage Score Max", "trainer/Q1 Predictions Mean", "trainer/replay_buffer_len"] + csv_util.check_equal(reference, output, keys) + +if __name__ == "__main__": + test_awac_gcrl_online() diff --git a/tests/regression/awac/gcrl/test_awac_gcrl_online.py b/tests/regression/awac/gcrl/test_awac_gcrl_online.py new file mode 100644 index 000000000..bfac1c5d4 --- /dev/null +++ b/tests/regression/awac/gcrl/test_awac_gcrl_online.py @@ -0,0 +1,37 @@ +"""Test AWAC GCRL offline+online. Requires the Sawyer environment from this multiworld +branch: https://github.com/vitchyr/multiworld/tree/ashvin-awac + +Data available for download: +https://drive.google.com/file/d/1rVoR3hrFjd6Ec5TLdPpC9Ii5eIM1j7Gc/view?usp=sharing +""" +import os +import sys + +from examples.awac.gcrl.pusher1 import main + +from rlkit.core import logger +from rlkit.testing import csv_util + +def test_awac_gcrl_online(): + cmd = "python examples/awac/gcrl/pusher1.py --1 --local --gpu --run 0 --seed 0 --debug" + sys.argv = cmd.split(" ")[1:] + main() + + # check if offline training results matches + reference_csv = "tests/regression/awac/gcrl/id0/pretrain_q.csv" + output_csv = os.path.join(logger.get_snapshot_dir(), "pretrain_q.csv") + output = csv_util.get_exp(output_csv) + reference = csv_util.get_exp(reference_csv) + keys = ["trainer/batch", "trainer/Advantage Score Max", "trainer/Q1 Predictions Mean", "trainer/replay_buffer_len"] + csv_util.check_equal(reference, output, keys) + + # check if online training results match + reference_csv = "tests/regression/awac/gcrl/id0/progress.csv" + output_csv = os.path.join(logger.get_snapshot_dir(), "progress.csv") + output = csv_util.get_exp(output_csv) + reference = csv_util.get_exp(reference_csv) + keys = ["epoch", "eval/Average Returns", "trainer/Advantage Score Max", "trainer/Q1 Predictions Mean", "trainer/replay_buffer_len"] + csv_util.check_equal(reference, output, keys) + +if __name__ == "__main__": + test_awac_gcrl_online() diff --git a/tests/regression/awac/hand/__init__.py b/tests/regression/awac/hand/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/regression/awac/hand/id0/debug.log b/tests/regression/awac/hand/id0/debug.log new file mode 100644 index 000000000..0090585d4 --- /dev/null +++ b/tests/regression/awac/hand/id0/debug.log @@ -0,0 +1,528 @@ +[id0] ------------------------------ --------------- +[id0] trainer/QF1 Loss 0.430996 +[id0] trainer/QF2 Loss 0.430787 +[id0] trainer/Policy Loss 37.697 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a/tests/regression/awac/hand/id0/pretrain_policy.csv b/tests/regression/awac/hand/id0/pretrain_policy.csv new file mode 100644 index 000000000..e69de29bb diff --git a/tests/regression/awac/hand/id0/pretrain_q.csv b/tests/regression/awac/hand/id0/pretrain_q.csv new file mode 100644 index 000000000..650d9d15b --- /dev/null +++ b/tests/regression/awac/hand/id0/pretrain_q.csv @@ -0,0 +1,2 @@ +trainer/Advantage Score Max,trainer/Advantage Score Mean,trainer/Advantage Score Min,trainer/Advantage Score Std,trainer/Advantage Weights Max,trainer/Advantage Weights Mean,trainer/Advantage Weights Min,trainer/Advantage Weights Std,trainer/Log Pis Max,trainer/Log Pis Mean,trainer/Log Pis Min,trainer/Log Pis Std,trainer/Policy Loss,trainer/Q Targets Max,trainer/Q Targets Mean,trainer/Q Targets Min,trainer/Q Targets Std,trainer/Q1 Predictions Max,trainer/Q1 Predictions Mean,trainer/Q1 Predictions Min,trainer/Q1 Predictions Std,trainer/Q2 Predictions Max,trainer/Q2 Predictions Mean,trainer/Q2 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a/tests/regression/awac/hand/id0/progress.csv b/tests/regression/awac/hand/id0/progress.csv new file mode 100644 index 000000000..97eaa86ae --- /dev/null +++ b/tests/regression/awac/hand/id0/progress.csv @@ -0,0 +1,6 @@ +epoch,eval/Actions Max,eval/Actions Mean,eval/Actions Min,eval/Actions Std,eval/Average Returns,eval/Num Paths,eval/Returns Max,eval/Returns Mean,eval/Returns Min,eval/Returns Std,eval/Rewards Max,eval/Rewards Mean,eval/Rewards Min,eval/Rewards Std,eval/num paths total,eval/num steps total,eval/path length Max,eval/path length Mean,eval/path length Min,eval/path length Std,expl/Actions Max,expl/Actions Mean,expl/Actions Min,expl/Actions Std,expl/Average Returns,expl/Num Paths,expl/Returns Max,expl/Returns Mean,expl/Returns Min,expl/Returns Std,expl/Rewards Max,expl/Rewards Mean,expl/Rewards Min,expl/Rewards Std,expl/num paths total,expl/num steps total,expl/path length Max,expl/path length Mean,expl/path length Min,expl/path length 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b/tests/regression/awac/hand/id0/script_name.txt @@ -0,0 +1 @@ +tests/regression/awac/hand/test_awac_hand.py \ No newline at end of file diff --git a/tests/regression/awac/hand/id0/variant.json b/tests/regression/awac/hand/id0/variant.json new file mode 100644 index 000000000..156d2e67e --- /dev/null +++ b/tests/regression/awac/hand/id0/variant.json @@ -0,0 +1,106 @@ +{ + "add_env_demos": true, + "add_env_offpolicy_data": true, + "algo_kwargs": { + "batch_size": 5, + "min_num_steps_before_training": 10, + "num_epochs": 5, + "num_eval_steps_per_epoch": 100, + "num_expl_steps_per_train_loop": 100, + "num_trains_per_train_loop": 10 + }, + "algorithm": "AWAC", + "collection_mode": "batch", + "debug": true, + "env_demo_path": { + "is_demo": true, + "obs_dict": true, + "path": "demos/icml2020/hand/pen2_sparse.npy" + }, + "env_id": "pen-binary-v0", + "env_offpolicy_data_path": { + "is_demo": false, + "obs_dict": false, + "path": "demos/icml2020/hand/pen_bc_sparse4.npy", + "train_split": 0.9 + 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b/tests/regression/awac/hand/id0_offline/script_name.txt @@ -0,0 +1 @@ +tests/regression/awac/hand/test_awac_hand_offline.py \ No newline at end of file diff --git a/tests/regression/awac/hand/id0_offline/variant.json b/tests/regression/awac/hand/id0_offline/variant.json new file mode 100644 index 000000000..9b91545ba --- /dev/null +++ b/tests/regression/awac/hand/id0_offline/variant.json @@ -0,0 +1,110 @@ +{ + "add_env_demos": false, + "add_env_offpolicy_data": false, + "algo_kwargs": { + "batch_size": 5, + "min_num_steps_before_training": 10, + "num_epochs": 5, + "num_eval_steps_per_epoch": 100, + "num_expl_steps_per_train_loop": 100, + "num_trains_per_train_loop": 10 + }, + "algorithm": "AWAC", + "batch_size": 5, + "collection_mode": "batch", + "debug": true, + "env_class": { + "$class": "rlkit.testing.stub_classes.StubEnv" + }, + "env_kwargs": { + "action_dim": 24, + "obs_dim": 45 + }, + "exp_id": 0, + "exp_name": "references/awac/hand/awac_offline1", + "load_demos": true, + 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