Source code for digideep.environment.common.vec_env.util

"""
The MIT License

Copyright (c) 2017 OpenAI (http://openai.com)

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"""


"""
Helpers for dealing with vectorized environments.
"""

from collections import OrderedDict

import gym
import numpy as np


[docs]def copy_obs_dict(obs): """ Deep-copy an observation dict. """ return {k: np.copy(v) for k, v in obs.items()}
[docs]def dict_to_obs(obs_dict): """ Convert an observation dict into a raw array if the original observation space was not a Dict space. """ if set(obs_dict.keys()) == {None}: return obs_dict[None] return obs_dict
[docs]def obs_space_info(obs_space): """ Get dict-structured information about a gym.Space. Returns: A tuple (keys, shapes, dtypes): keys: a list of dict keys. shapes: a dict mapping keys to shapes. dtypes: a dict mapping keys to dtypes. """ if isinstance(obs_space, gym.spaces.Dict): assert isinstance(obs_space.spaces, OrderedDict) subspaces = obs_space.spaces else: subspaces = {None: obs_space} keys = [] shapes = {} dtypes = {} for key, box in subspaces.items(): keys.append(key) shapes[key] = box.shape dtypes[key] = box.dtype return keys, shapes, dtypes
[docs]def obs_to_dict(obs): """ Convert an observation into a dict. """ if isinstance(obs, dict): return obs return {None: obs}