""" Helpers for dealing with vectorized environments. """ from typing import Any import numpy as np from gymnasium import spaces from stable_baselines3.common.preprocessing import check_for_nested_spaces from stable_baselines3.common.vec_env.base_vec_env import VecEnvObs def dict_to_obs(obs_space: spaces.Space, obs_dict: dict[Any, np.ndarray]) -> VecEnvObs: """ Convert an internal representation raw_obs into the appropriate type specified by space. :param obs_space: an observation space. :param obs_dict: a dict of numpy arrays. :return: returns an observation of the same type as space. If space is Dict, function is identity; if space is Tuple, converts dict to Tuple; otherwise, space is unstructured and returns the value raw_obs[None]. """ if isinstance(obs_space, spaces.Dict): return obs_dict elif isinstance(obs_space, spaces.Tuple): assert len(obs_dict) == len(obs_space.spaces), "size of observation does not match size of observation space" return tuple(obs_dict[i] for i in range(len(obs_space.spaces))) else: assert set(obs_dict.keys()) == {None}, "multiple observation keys for unstructured observation space" return obs_dict[None] def obs_space_info(obs_space: spaces.Space) -> tuple[list[str], dict[Any, tuple[int, ...]], dict[Any, np.dtype]]: """ Get dict-structured information about a gym.Space. Dict spaces are represented directly by their dict of subspaces. Tuple spaces are converted into a dict with keys indexing into the tuple. Unstructured spaces are represented by {None: obs_space}. :param obs_space: an observation space :return: 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. """ check_for_nested_spaces(obs_space) if isinstance(obs_space, spaces.Dict): assert isinstance(obs_space.spaces, dict), "Dict space must have ordered subspaces" subspaces = obs_space.spaces elif isinstance(obs_space, spaces.Tuple): subspaces = {i: space for i, space in enumerate(obs_space.spaces)} # type: ignore[assignment,misc] else: assert not hasattr(obs_space, "spaces"), f"Unsupported structured space '{type(obs_space)}'" subspaces = {None: obs_space} # type: ignore[assignment,dict-item] keys = [] shapes = {} dtypes = {} for key, box in subspaces.items(): keys.append(key) shapes[key] = box.shape dtypes[key] = box.dtype return keys, shapes, dtypes # type: ignore[return-value]