emdp.emdp_gym package

Subpackages

• emdp.emdp_gym.example_envs package
  • Submodules
  • emdp.emdp_gym.example_envs.wrapped_examples module
  • Module contents

Submodules

emdp.emdp_gym.gym_wrap module

Allows using emdp as a gym environment.

class emdp.emdp_gym.gym_wrap.GymToMDP(mdp, observation_one_hot=True)

Bases: gym.core.Env

action_space: spaces.Space[ActType]

maybe_convert_state(state)

observation_space: spaces.Space[ObsType]

render()

Renders the environment.

The set of supported modes varies per environment. (And some third-party environments may 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.

Parameters

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 environment to an initial state and returns an initial observation.

This method should also reset the environment’s random number generator(s) if seed is an integer or if the environment has not yet initialized a random number generator. If the environment already has a random number generator and reset is called with seed=None, the RNG should not be reset. Moreover, reset should (in the typical use case) be called with an integer seed right after initialization and then never again.

Returns

the initial observation. info (optional dictionary): a dictionary containing extra information, this is only returned if return_info is set to true

Return type

observation (object)

seed(seed)

Sets the seed for this env’s random number generator(s).

Note

Some environments use multiple pseudorandom number generators. We want to capture all such seeds used in order to ensure that there aren’t accidental correlations between multiple generators.

Returns

Returns the list of seeds used in this env’s random

number generators. The first value in the list should be the “main” seed, or the value which a reproducer should pass to ‘seed’. Often, the main seed equals the provided ‘seed’, but this won’t be true if seed=None, for example.

Return type

list<bigint>

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).

Parameters

action (object) – an action provided by the agent

Returns

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, logging, and sometimes learning)

Return type

observation (object)

emdp.emdp_gym.gym_wrap.gymify(mdp, **kwargs)

Module contents