rlmaster

Environments and RL algorithms.

Environment

An environment is defined by:

• Simulator: an object of type BaseSimulator that simulates the effect of agent's actions in the environment and provides observations.

• Initializer: an object of type BaseInitializer that defines how the epsiode should be initialized.

• Observer: an object of type BaseObserver that choses the desired representation from the simulator. It is useful when it is required to train an agent in the same environment but with different observations (for eg: training with pixels or with state features)

• Rewarder: an object of type BaseRewarder that describes the reward function. It is useful when the agent needs to be trained on the same environment but with different reward functions.

• ActionProcessor: an object of type BaseAction that defines functions that process the action. Generally simulator works by applying continous forces/torque, however the desired action space might be discrete or continuous. BaseAction objects allow the user to easily switch between different action spaces.

Interactive Mode

To run environments in interactive mode, a function mapping the keyboard inputs into commands supplied to the agent must be defined. This function is typically called, str2action in rlmaster repository. The environment can be run in interactive mode in the following way:

from envs.mujoco_envs import move_single_env
env = move_single_env.get_environment(actType='ContinuousAction', imSz=480)
env.interactive(move_single_env.str2action)

You can use the commands, w, s, d, a to move the agent and q to quit the interactive mode.

Cheetah in Interactive Mode

No interface has been developed yet, but after creating an instance of SimpleStackerAgent in from stacker_agent, calling _setup_renderer and render will open a display window. You can step by entering an xy position that will move the second block there instantly. Calling render again will show the updated positions of the blocks. They are initialized at position (0,0) if not called with the initializer.

##Environment in openAI gym format

from envs import move_agent
from core import gym_wrapper
env = move_agent.get_environment()
gymEnv = gym_wwapper.GymWrapper(env)