mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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15049e22d7
* initial draft of optional info dict in reset function, implemented for cartpole, tests seem to be passing * merged core.py * updated return type annotation for reset function in core.py * optional metadata with return_info from reset added for all first party environments, with corresponding tests. Incomplete implementation for wrappers and vector wrappers * removed Optional type for return_info arguments * added tests for return_info to normalize wrapper and sync_vector_env * autoformatted using black * added optional reset metadata tests to several wrappers * added return_info capability to async_vector_env.py and test to verify functionality * added optional return_info test for record_video.py * removed tests for mujoco environments * autoformatted * improved test coverage for optional reset return_info * re-removed unit test envs accidentally reintroduced in merge * removed unnecessary import * changes based on code-review * small fix to core wrapper typing and autoformatted record_epsisode_stats * small change to pass flake8 style
333 lines
10 KiB
Python
333 lines
10 KiB
Python
from __future__ import annotations
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from abc import abstractmethod
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from typing import TypeVar, Generic, Tuple, Union, Optional, SupportsFloat
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import gym
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from gym import error, spaces
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from gym.utils import closer, seeding
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from gym.logger import deprecation
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ObsType = TypeVar("ObsType")
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ActType = TypeVar("ActType")
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class Env(Generic[ObsType, ActType]):
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"""The main OpenAI Gym class. It encapsulates an environment with
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arbitrary behind-the-scenes dynamics. An environment can be
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partially or fully observed.
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The main API methods that users of this class need to know are:
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step
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reset
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render
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close
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seed
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And set the following attributes:
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action_space: The Space object corresponding to valid actions
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observation_space: The Space object corresponding to valid observations
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reward_range: A tuple corresponding to the min and max possible rewards
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Note: a default reward range set to [-inf,+inf] already exists. Set it if you want a narrower range.
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The methods are accessed publicly as "step", "reset", etc...
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"""
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# Set this in SOME subclasses
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metadata = {"render.modes": []}
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reward_range = (-float("inf"), float("inf"))
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spec = None
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# Set these in ALL subclasses
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action_space: spaces.Space[ActType]
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observation_space: spaces.Space[ObsType]
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# Created
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np_random = None
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@abstractmethod
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def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
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"""Run one timestep of the environment's dynamics. When end of
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episode is reached, you are responsible for calling `reset()`
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to reset this environment's state.
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Accepts an action and returns a tuple (observation, reward, done, info).
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Args:
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action (object): an action provided by the agent
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Returns:
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observation (object): agent's observation of the current environment
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reward (float) : amount of reward returned after previous action
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done (bool): whether the episode has ended, in which case further step() calls will return undefined results
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info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)
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"""
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raise NotImplementedError
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@abstractmethod
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def reset(
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self,
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*,
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seed: Optional[int] = None,
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return_info: bool = False,
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options: Optional[dict] = None,
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) -> Union[ObsType, tuple[ObsType, dict]]:
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"""Resets the environment to an initial state and returns an initial
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observation.
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Note that this function should not reset the environment's random
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number generator(s); random variables in the environment's state should
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be sampled independently between multiple calls to `reset()`. In other
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words, each call of `reset()` should yield an environment suitable for
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a new episode, independent of previous episodes.
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Returns:
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observation (object): the initial observation.
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info (optional dictionary): a dictionary containing extra information, this is only returned if return_info is set to true
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"""
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# Initialize the RNG if it's the first reset, or if the seed is manually passed
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if seed is not None or self.np_random is None:
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self.np_random, seed = seeding.np_random(seed)
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@abstractmethod
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def render(self, mode="human"):
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"""Renders the environment.
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The set of supported modes varies per environment. (And some
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environments do not support rendering at all.) By convention,
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if mode is:
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- human: render to the current display or terminal and
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return nothing. Usually for human consumption.
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- rgb_array: Return an numpy.ndarray with shape (x, y, 3),
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representing RGB values for an x-by-y pixel image, suitable
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for turning into a video.
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- ansi: Return a string (str) or StringIO.StringIO containing a
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terminal-style text representation. The text can include newlines
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and ANSI escape sequences (e.g. for colors).
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Note:
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Make sure that your class's metadata 'render.modes' key includes
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the list of supported modes. It's recommended to call super()
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in implementations to use the functionality of this method.
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Args:
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mode (str): the mode to render with
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Example:
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class MyEnv(Env):
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metadata = {'render.modes': ['human', 'rgb_array']}
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def render(self, mode='human'):
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if mode == 'rgb_array':
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return np.array(...) # return RGB frame suitable for video
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elif mode == 'human':
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... # pop up a window and render
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else:
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super(MyEnv, self).render(mode=mode) # just raise an exception
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"""
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raise NotImplementedError
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def close(self):
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"""Override close in your subclass to perform any necessary cleanup.
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Environments will automatically close() themselves when
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garbage collected or when the program exits.
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"""
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pass
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def seed(self, seed=None):
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"""Sets the seed for this env's random number generator(s).
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Note:
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Some environments use multiple pseudorandom number generators.
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We want to capture all such seeds used in order to ensure that
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there aren't accidental correlations between multiple generators.
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Returns:
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list<bigint>: Returns the list of seeds used in this env's random
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number generators. The first value in the list should be the
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"main" seed, or the value which a reproducer should pass to
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'seed'. Often, the main seed equals the provided 'seed', but
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this won't be true if seed=None, for example.
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"""
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deprecation(
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"Function `env.seed(seed)` is marked as deprecated and will be removed in the future. "
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"Please use `env.reset(seed=seed) instead."
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)
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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@property
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def unwrapped(self) -> Env:
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"""Completely unwrap this env.
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Returns:
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gym.Env: The base non-wrapped gym.Env instance
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"""
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return self
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def __str__(self):
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if self.spec is None:
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return f"<{type(self).__name__} instance>"
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else:
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return f"<{type(self).__name__}<{self.spec.id}>>"
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def __enter__(self):
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"""Support with-statement for the environment."""
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return self
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def __exit__(self, *args):
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"""Support with-statement for the environment."""
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self.close()
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# propagate exception
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return False
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class Wrapper(Env[ObsType, ActType]):
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"""Wraps the environment to allow a modular transformation.
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This class is the base class for all wrappers. The subclass could override
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some methods to change the behavior of the original environment without touching the
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original code.
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.. note::
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Don't forget to call ``super().__init__(env)`` if the subclass overrides :meth:`__init__`.
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"""
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def __init__(self, env: Env):
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self.env = env
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self._action_space: spaces.Space | None = None
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self._observation_space: spaces.Space | None = None
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self._reward_range: tuple[SupportsFloat, SupportsFloat] | None = None
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self._metadata: dict | None = None
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def __getattr__(self, name):
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if name.startswith("_"):
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raise AttributeError(f"attempted to get missing private attribute '{name}'")
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return getattr(self.env, name)
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@property
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def spec(self):
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return self.env.spec
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@classmethod
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def class_name(cls):
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return cls.__name__
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@property
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def action_space(self) -> spaces.Space[ActType]:
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if self._action_space is None:
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return self.env.action_space
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return self._action_space
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@action_space.setter
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def action_space(self, space):
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self._action_space = space
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@property
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def observation_space(self) -> spaces.Space:
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if self._observation_space is None:
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return self.env.observation_space
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return self._observation_space
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@observation_space.setter
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def observation_space(self, space):
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self._observation_space = space
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@property
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def reward_range(self) -> tuple[SupportsFloat, SupportsFloat]:
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if self._reward_range is None:
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return self.env.reward_range
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return self._reward_range
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@reward_range.setter
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def reward_range(self, value):
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self._reward_range = value
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@property
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def metadata(self) -> dict:
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if self._metadata is None:
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return self.env.metadata
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return self._metadata
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@metadata.setter
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def metadata(self, value):
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self._metadata = value
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def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
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return self.env.step(action)
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def reset(self, **kwargs) -> Union[ObsType, tuple[ObsType, dict]]:
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return self.env.reset(**kwargs)
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def render(self, mode="human", **kwargs):
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return self.env.render(mode, **kwargs)
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def close(self):
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return self.env.close()
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def seed(self, seed=None):
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return self.env.seed(seed)
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def __str__(self):
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return f"<{type(self).__name__}{self.env}>"
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def __repr__(self):
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return str(self)
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@property
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def unwrapped(self) -> Env:
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return self.env.unwrapped
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class ObservationWrapper(Wrapper):
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def reset(self, **kwargs):
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observation = self.env.reset(**kwargs)
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return self.observation(observation)
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def step(self, action):
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observation, reward, done, info = self.env.step(action)
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return self.observation(observation), reward, done, info
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@abstractmethod
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def observation(self, observation):
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raise NotImplementedError
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class RewardWrapper(Wrapper):
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def reset(self, **kwargs):
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return self.env.reset(**kwargs)
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def step(self, action):
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observation, reward, done, info = self.env.step(action)
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return observation, self.reward(reward), done, info
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@abstractmethod
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def reward(self, reward):
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raise NotImplementedError
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class ActionWrapper(Wrapper):
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def reset(self, **kwargs):
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return self.env.reset(**kwargs)
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def step(self, action):
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return self.env.step(self.action(action))
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@abstractmethod
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def action(self, action):
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raise NotImplementedError
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@abstractmethod
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def reverse_action(self, action):
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raise NotImplementedError
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