File size: 1,513 Bytes
5960497 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | """
Helpers for dealing with vectorized environments.
"""
from collections import OrderedDict
import gym
import numpy as np
def copy_obs_dict(obs):
"""
Deep-copy an observation dict.
"""
return {k: np.copy(v) for k, v in obs.items()}
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
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
elif isinstance(obs_space, gym.spaces.Tuple):
assert isinstance(obs_space.spaces, tuple)
subspaces = {i: obs_space.spaces[i] for i in range(len(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
def obs_to_dict(obs):
"""
Convert an observation into a dict.
"""
if isinstance(obs, dict):
return obs
return {None: obs}
|