mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-19 05:25:54 +00:00
f4ae35ea73
* Wrappers - Added 'wrappers' and 'step_count' property + monitor support * Removed step_count and wrappers from api and monitor * Removed wrappers and name property
355 lines
12 KiB
Python
355 lines
12 KiB
Python
import logging
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logger = logging.getLogger(__name__)
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import numpy as np
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import weakref
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from gym import error, monitoring
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from gym.utils import closer, reraise
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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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configure
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seed
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When implementing an environment, override the following methods
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in your subclass:
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_step
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_reset
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_render
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_close
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_configure
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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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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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def __new__(cls, *args, **kwargs):
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# We use __new__ since we want the env author to be able to
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# override __init__ without remembering to call super.
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env = super(Env, cls).__new__(cls)
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env._env_closer_id = env_closer.register(env)
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env._closed = False
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env._configured = False
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env._unwrapped = None
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# Will be automatically set when creating an environment via 'make'
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env.spec = None
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return env
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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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# Override in SOME subclasses
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def _close(self):
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pass
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def _configure(self):
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pass
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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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# Override in ALL subclasses
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def _step(self, action): raise NotImplementedError
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def _reset(self): raise NotImplementedError
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def _render(self, mode='human', close=False):
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if close:
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return
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raise NotImplementedError
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def _seed(self, seed=None): return []
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@property
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def monitor(self):
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"""Lazily creates a monitor instance.
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We do this lazily rather than at environment creation time
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since when the monitor closes, we need remove the existing
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monitor but also make it easy to start a new one. We could
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still just forcibly create a new monitor instance on old
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monitor close, but that seems less clean.
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"""
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if not hasattr(self, '_monitor'):
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self._monitor = monitoring.Monitor(self)
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return self._monitor
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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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self.monitor._before_step(action)
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observation, reward, done, info = self._step(action)
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done = self.monitor._after_step(observation, reward, done, info)
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return observation, reward, done, info
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def reset(self):
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"""
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Resets the state of the environment and returns an initial observation.
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Returns:
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observation (object): the initial observation of the space. (Initial reward is assumed to be 0.)
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"""
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if self.metadata.get('configure.required') and not self._configured:
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raise error.Error("{} requires calling 'configure()' before 'reset()'".format(self))
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self.monitor._before_reset()
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observation = self._reset()
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self.monitor._after_reset(observation)
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return observation
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def render(self, mode='human', close=False):
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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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if close:
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return self._render(close=close)
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# This code can be useful for calling super() in a subclass.
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modes = self.metadata.get('render.modes', [])
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if len(modes) == 0:
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raise error.UnsupportedMode('{} does not support rendering (requested mode: {})'.format(self, mode))
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elif mode not in modes:
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raise error.UnsupportedMode('Unsupported rendering mode: {}. (Supported modes for {}: {})'.format(mode, self, modes))
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return self._render(mode=mode, close=close)
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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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# _closed will be missing if this instance is still
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# initializing.
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if not hasattr(self, '_closed') or self._closed:
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return
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self._close()
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env_closer.unregister(self._env_closer_id)
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# If an error occurs before this line, it's possible to
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# end up with double close.
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self._closed = True
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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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return self._seed(seed)
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def configure(self, *args, **kwargs):
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"""Provides runtime configuration to the environment.
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This configuration should consist of data that tells your
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environment how to run (such as an address of a remote server,
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or path to your ImageNet data). It should not affect the
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semantics of the environment.
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"""
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self._configured = True
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try:
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return self._configure(*args, **kwargs)
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except TypeError as e:
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# It can be confusing if you have the wrong environment
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# and try calling with unsupported arguments, since your
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# stack trace will only show core.py.
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if self.spec:
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reraise(suffix='(for {})'.format(self.spec.id))
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else:
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raise
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def build(self):
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"""[EXPERIMENTAL: may be removed in a later version of Gym] Builds an
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environment by applying any provided wrappers, with the
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outmost wrapper supplied first. This method is automatically
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invoked by 'gym.make', and should be manually invoked if
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instantiating an environment by hand.
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Notes:
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The default implementation will wrap the environment in the
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list of wrappers provided in self.metadata['wrappers'], in reverse
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order. So for example, given:
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class FooEnv(gym.Env):
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metadata = {
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'wrappers': [Wrapper1, Wrapper2]
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}
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Calling 'env.build' will return 'Wrapper1(Wrapper2(env))'.
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Returns:
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gym.Env: A potentially wrapped environment instance.
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"""
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wrapped = self
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for wrapper in reversed(self.metadata.get('wrappers', [])):
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wrapped = wrapper(wrapped)
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return wrapped
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@property
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def unwrapped(self):
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"""Avoid refcycles by making this into a property."""
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if self._unwrapped is not None:
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return self._unwrapped
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else:
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return self
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def __del__(self):
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self.close()
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def __str__(self):
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return '<{} instance>'.format(type(self).__name__)
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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 sample(self, seed=0):
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"""
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Uniformly randomly sample a random elemnt 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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class Wrapper(Env):
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def __new__(cls, env, *args, **kwargs):
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# We use __new__ since we want the wrapper author to be able to
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# override __init__ without remembering to call super.
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wrapper = super(Wrapper, cls).__new__(cls)
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wrapper.env = env
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wrapper.metadata = env.metadata
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wrapper.action_space = env.action_space
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wrapper.observation_space = env.observation_space
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wrapper.reward_range = env.reward_range
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wrapper.spec = env.spec
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wrapper._unwrapped = env.unwrapped
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return wrapper
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# Overridable
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def __init__(self, env):
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pass
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def _step(self, 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 _render(self, mode='human', close=False):
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return self.env.render(mode, close)
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def _close(self):
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return self.env.close()
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def _configure(self, *args, **kwargs):
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return self.env.configure(*args, **kwargs)
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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 '<{}{} instance>'.format(type(self).__name__, self.env)
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