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309 lines
10 KiB
Python
309 lines
10 KiB
Python
from gym import logger
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import numpy as np
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from gym import error
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from gym.utils import closer
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env_closer = closer.Closer()
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# Env-related abstractions
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class Env(object):
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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.. The
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non-underscored versions are wrapper methods to which we may add
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functionality over time.
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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 = (-np.inf, np.inf)
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spec = None
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# Set these in ALL subclasses
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action_space = None
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observation_space = None
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def step(self, action):
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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 environment
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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 (boolean): 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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def reset(self):
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"""Resets the state of the environment and returns an initial observation.
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Returns: observation (object): the initial observation of the
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space.
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"""
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raise NotImplementedError
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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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close (bool): close all open renderings
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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 is '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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return
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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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logger.warn("Could not seed environment %s", self)
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return
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@property
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def unwrapped(self):
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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 '<{} instance>'.format(type(self).__name__)
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else:
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return '<{}<{}>>'.format(type(self).__name__, self.spec.id)
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# Space-related abstractions
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class Space(object):
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"""Defines the observation and action spaces, so you can write generic
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code that applies to any Env. For example, you can choose a random
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action.
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"""
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def __init__(self, shape=None, dtype=None):
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self.shape = None if shape is None else tuple(shape)
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self.dtype = None if dtype is None else np.dtype(dtype)
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def sample(self):
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"""
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Uniformly randomly sample a random element of this space
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"""
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raise NotImplementedError
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def contains(self, x):
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"""
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Return boolean specifying if x is a valid
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member of this space
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"""
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raise NotImplementedError
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def to_jsonable(self, sample_n):
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"""Convert a batch of samples from this space to a JSONable data type."""
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# By default, assume identity is JSONable
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return sample_n
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def from_jsonable(self, sample_n):
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"""Convert a JSONable data type to a batch of samples from this space."""
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# By default, assume identity is JSONable
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return sample_n
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warn_once = True
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def deprecated_warn_once(text):
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global warn_once
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if not warn_once: return
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warn_once = False
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logger.warn(text)
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class Wrapper(Env):
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env = None
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def __init__(self, env):
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self.env = env
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self.action_space = self.env.action_space
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self.observation_space = self.env.observation_space
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self.reward_range = self.env.reward_range
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self.metadata = self.env.metadata
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self._warn_double_wrap()
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@classmethod
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def class_name(cls):
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return cls.__name__
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def _warn_double_wrap(self):
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env = self.env
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while True:
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if isinstance(env, Wrapper):
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if env.class_name() == self.class_name():
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raise error.DoubleWrapperError("Attempted to double wrap with Wrapper: {}".format(self.__class__.__name__))
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env = env.env
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else:
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break
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def step(self, action):
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if hasattr(self, "_step"):
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deprecated_warn_once("%s doesn't implement 'step' method, but it implements deprecated '_step' method." % type(self))
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self.step = self._step
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return self.step(action)
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else:
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deprecated_warn_once("%s doesn't implement 'step' method, " % type(self) +
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"which is required for wrappers derived directly from Wrapper. Deprecated default implementation is used.")
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return self.env.step(action)
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def reset(self, **kwargs):
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if hasattr(self, "_reset"):
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deprecated_warn_once("%s doesn't implement 'reset' method, but it implements deprecated '_reset' method." % type(self))
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self.reset = self._reset
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return self._reset(**kwargs)
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else:
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deprecated_warn_once("%s doesn't implement 'reset' method, " % type(self) +
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"which is required for wrappers derived directly from Wrapper. Deprecated default implementation is used.")
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return self.env.reset(**kwargs)
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def render(self, mode='human'):
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return self.env.render(mode)
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def close(self):
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if self.env:
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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 '<{}{}>'.format(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):
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return self.env.unwrapped
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@property
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def spec(self):
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return self.env.spec
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class ObservationWrapper(Wrapper):
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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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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 observation(self, observation):
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deprecated_warn_once("%s doesn't implement 'observation' method. Maybe it implements deprecated '_observation' method." % type(self))
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return self._observation(observation)
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class RewardWrapper(Wrapper):
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def reset(self):
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return self.env.reset()
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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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def reward(self, reward):
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deprecated_warn_once("%s doesn't implement 'reward' method. Maybe it implements deprecated '_reward' method." % type(self))
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return self._reward(reward)
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class ActionWrapper(Wrapper):
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def step(self, action):
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action = self.action(action)
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return self.env.step(action)
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def reset(self):
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return self.env.reset()
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def action(self, action):
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deprecated_warn_once("%s doesn't implement 'action' method. Maybe it implements deprecated '_action' method." % type(self))
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return self._action(action)
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def reverse_action(self, action):
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deprecated_warn_once("%s doesn't implement 'reverse_action' method. Maybe it implements deprecated '_reverse_action' method." % type(self))
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return self._reverse_action(action)
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