.rst .pdf

mdp_playground.envs.gym_env_wrapper.GymEnvWrapper

class mdp_playground.envs.gym_env_wrapper.GymEnvWrapper(env, **config)

Bases: gym.core.Env

Wraps an OpenAI Gym environment to be able to modify its dimensions corresponding to MDP Playground. The documentation for the supported dimensions below can be found in mdp_playground/envs/rl_toy_env.py.

Currently supported dimensions:

transition noise (discrete) reward delay reward noise

Also supports wrapping with AtariPreprocessing from OpenAI Gym or wrap_deepmind from Ray Rllib.

__init__(env, **config)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(env, **config)	Initialize self.
close()	Override close in your subclass to perform any necessary cleanup.
render([mode])	Renders the environment.
reset()	Resets the state of the environment and returns an initial observation.
seed([seed])	Initialises the Numpy RNG for the environment by calling a utility for this in Gym.
step(action)	Run one timestep of the environment’s dynamics.

Attributes

action_space
metadata
observation_space
reward_range
spec
unwrapped	Completely unwrap this env.

close()

Override close in your subclass to perform any necessary cleanup.

Environments will automatically close() themselves when garbage collected or when the program exits.

render(mode='human')

Renders the environment.

The set of supported modes varies per environment. (And some environments do not support rendering at all.) By convention, if mode is:

• human: render to the current display or terminal and return nothing. Usually for human consumption.

• rgb_array: Return an numpy.ndarray with shape (x, y, 3), representing RGB values for an x-by-y pixel image, suitable for turning into a video.

• ansi: Return a string (str) or StringIO.StringIO containing a terminal-style text representation. The text can include newlines and ANSI escape sequences (e.g. for colors).

Note:

Make sure that your class’s metadata ‘render.modes’ key includes

the list of supported modes. It’s recommended to call super() in implementations to use the functionality of this method.

Args:

mode (str): the mode to render with

Example:

class MyEnv(Env):

metadata = {‘render.modes’: [‘human’, ‘rgb_array’]}

def render(self, mode=’human’):

if mode == ‘rgb_array’:

return np.array(…) # return RGB frame suitable for video

elif mode == ‘human’:

… # pop up a window and render

else:

super(MyEnv, self).render(mode=mode) # just raise an exception

reset()

Resets the state of the environment and returns an initial observation.

Returns:

observation (object): the initial observation.

seed(seed=None)

Initialises the Numpy RNG for the environment by calling a utility for this in Gym.

Parameters

seed (int) – seed to initialise the np_random instance held by the environment. Cannot use numpy.int64 or similar because Gym doesn’t accept it.

Returns

The seed returned by Gym

Return type

int

step(action)

Run one timestep of the environment’s dynamics. When end of episode is reached, you are responsible for calling reset() to reset this environment’s state.

Accepts an action and returns a tuple (observation, reward, done, info).

Args:

action (object): an action provided by the agent

Returns:

observation (object): agent’s observation of the current environment reward (float) : amount of reward returned after previous action done (bool): whether the episode has ended, in which case further step() calls will return undefined results info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

property unwrapped

Completely unwrap this env.

Returns:

gym.Env: The base non-wrapped gym.Env instance

mdp_playground.envs.gym_env_wrapper mdp_playground.envs.mujoco_env_wrapper