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from gym import logger
import gym
from gym import error
from gym.utils import closer
env_closer = closer.Closer()
# Env-related abstractions
class Env(object):
"""The main OpenAI Gym class. It encapsulates an environment with
arbitrary behind-the-scenes dynamics. An environment can be
partially or fully observed.
The main API methods that users of this class need to know are:
step
reset
render
close
seed
And set the following attributes:
action_space: The Space object corresponding to valid actions
observation_space: The Space object corresponding to valid observations
reward_range: A tuple corresponding to the min and max possible rewards
Note: a default reward range set to [-inf,+inf] already exists. Set it if you want a narrower range.
The methods are accessed publicly as "step", "reset", etc.. The
non-underscored versions are wrapper methods to which we may add
functionality over time.
"""
# Set this in SOME subclasses
metadata = {'render.modes': []}
reward_range = (-float('inf'), float('inf'))
spec = None
# Set these in ALL subclasses
action_space = None
observation_space = None
def step(self, 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 environment
Returns:
observation (object): agent's observation of the current environment
reward (float) : amount of reward returned after previous action
done (boolean): 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)
"""
raise NotImplementedError
def reset(self):
"""Resets the state of the environment and returns an initial observation.
Returns: observation (object): the initial observation of the
space.
"""
raise NotImplementedError
def render(self, 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
close (bool): close all open renderings
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 is 'human':
... # pop up a window and render
else:
super(MyEnv, self).render(mode=mode) # just raise an exception
"""
raise NotImplementedError
def close(self):
"""Override _close in your subclass to perform any necessary cleanup.
Environments will automatically close() themselves when
garbage collected or when the program exits.
"""
return
def seed(self, seed=None):
"""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:
list<bigint>: 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.
"""
logger.warn("Could not seed environment %s", self)
return
@property
def unwrapped(self):
"""Completely unwrap this env.
Returns:
gym.Env: The base non-wrapped gym.Env instance
"""
return self
def __str__(self):
if self.spec is None:
return '<{} instance>'.format(type(self).__name__)
else:
return '<{}<{}>>'.format(type(self).__name__, self.spec.id)
class GoalEnv(Env):
"""A goal-based environment. It functions just as any regular OpenAI Gym environment but it
imposes a required structure on the observation_space. More concretely, the observation
space is required to contain at least three elements, namely `observation`, `desired_goal`, and
`achieved_goal`. Here, `desired_goal` specifies the goal that the agent should attempt to achieve.
`achieved_goal` is the goal that it currently achieved instead. `observation` contains the
actual observations of the environment as per usual.
"""
def reset(self):
# Enforce that each GoalEnv uses a Goal-compatible observation space.
if not isinstance(self.observation_space, gym.spaces.Dict):
raise error.Error('GoalEnv requires an observation space of type gym.spaces.Dict')
result = super(GoalEnv, self).reset()
for key in ['observation', 'achieved_goal', 'desired_goal']:
if key not in result:
raise error.Error('GoalEnv requires the "{}" key to be part of the observation dictionary.'.format(key))
return result
def compute_reward(self, achieved_goal, desired_goal, info):
"""Compute the step reward. This externalizes the reward function and makes
it dependent on an a desired goal and the one that was achieved. If you wish to include
additional rewards that are independent of the goal, you can include the necessary values
to derive it in info and compute it accordingly.
Args:
achieved_goal (object): the goal that was achieved during execution
desired_goal (object): the desired goal that we asked the agent to attempt to achieve
info (dict): an info dictionary with additional information
Returns:
float: The reward that corresponds to the provided achieved goal w.r.t. to the desired
goal. Note that the following should always hold true:
ob, reward, done, info = env.step()
assert reward == env.compute_reward(ob['achieved_goal'], ob['goal'], info)
"""
raise NotImplementedError()
# Space-related abstractions
class Space(object):
"""Defines the observation and action spaces, so you can write generic
code that applies to any Env. For example, you can choose a random
action.
"""
def __init__(self, shape=None, dtype=None):
import numpy as np # takes about 300-400ms to import, so we load lazily
self.shape = None if shape is None else tuple(shape)
self.dtype = None if dtype is None else np.dtype(dtype)
def sample(self):
"""
Uniformly randomly sample a random element of this space
"""
raise NotImplementedError
def contains(self, x):
"""
Return boolean specifying if x is a valid
member of this space
"""
raise NotImplementedError
def to_jsonable(self, sample_n):
"""Convert a batch of samples from this space to a JSONable data type."""
# By default, assume identity is JSONable
return sample_n
def from_jsonable(self, sample_n):
"""Convert a JSONable data type to a batch of samples from this space."""
# By default, assume identity is JSONable
return sample_n
warn_once = True
def deprecated_warn_once(text):
global warn_once
if not warn_once: return
warn_once = False
logger.warn(text)
class Wrapper(Env):
env = None
def __init__(self, env):
self.env = env
self.action_space = self.env.action_space
self.observation_space = self.env.observation_space
self.reward_range = self.env.reward_range
self.metadata = self.env.metadata
@classmethod
def class_name(cls):
return cls.__name__
def step(self, action):
if hasattr(self, "_step"):
deprecated_warn_once("%s doesn't implement 'step' method, but it implements deprecated '_step' method." % type(self))
self.step = self._step
return self.step(action)
else:
deprecated_warn_once("%s doesn't implement 'step' method, " % type(self) +
"which is required for wrappers derived directly from Wrapper. Deprecated default implementation is used.")
return self.env.step(action)
def reset(self, **kwargs):
if hasattr(self, "_reset"):
deprecated_warn_once("%s doesn't implement 'reset' method, but it implements deprecated '_reset' method." % type(self))
self.reset = self._reset
return self._reset(**kwargs)
else:
deprecated_warn_once("%s doesn't implement 'reset' method, " % type(self) +
"which is required for wrappers derived directly from Wrapper. Deprecated default implementation is used.")
return self.env.reset(**kwargs)
def render(self, mode='human', **kwargs):
return self.env.render(mode, **kwargs)
def close(self):
if self.env:
return self.env.close()
def seed(self, seed=None):
return self.env.seed(seed)
def compute_reward(self, achieved_goal, desired_goal, info):
return self.env.compute_reward(achieved_goal, desired_goal, info)
def __str__(self):
return '<{}{}>'.format(type(self).__name__, self.env)
def __repr__(self):
return str(self)
@property
def unwrapped(self):
return self.env.unwrapped
@property
def spec(self):
return self.env.spec
class ObservationWrapper(Wrapper):
def step(self, action):
observation, reward, done, info = self.env.step(action)
return self.observation(observation), reward, done, info
def reset(self, **kwargs):
observation = self.env.reset(**kwargs)
return self.observation(observation)
def observation(self, observation):
deprecated_warn_once("%s doesn't implement 'observation' method. Maybe it implements deprecated '_observation' method." % type(self))
return self._observation(observation)
class RewardWrapper(Wrapper):
def reset(self):
return self.env.reset()
def step(self, action):
observation, reward, done, info = self.env.step(action)
return observation, self.reward(reward), done, info
def reward(self, reward):
deprecated_warn_once("%s doesn't implement 'reward' method. Maybe it implements deprecated '_reward' method." % type(self))
return self._reward(reward)
class ActionWrapper(Wrapper):
def step(self, action):
action = self.action(action)
return self.env.step(action)
def reset(self):
return self.env.reset()
def action(self, action):
deprecated_warn_once("%s doesn't implement 'action' method. Maybe it implements deprecated '_action' method." % type(self))
return self._action(action)
def reverse_action(self, action):
deprecated_warn_once("%s doesn't implement 'reverse_action' method. Maybe it implements deprecated '_reverse_action' method." % type(self))
return self._reverse_action(action)