import warnings
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Callable, Dict, List, Optional, Sequence, Type
import gymnasium as gym
import numpy as np
from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvIndices, VecEnvObs, VecEnvStepReturn
from stable_baselines3.common.vec_env.patch_gym import _patch_env
from stable_baselines3.common.vec_env.util import copy_obs_dict, dict_to_obs, obs_space_info
[docs]class DummyVecEnv(VecEnv):
"""
Creates a simple vectorized wrapper for multiple environments, calling each environment in sequence on the current
Python process. This is useful for computationally simple environment such as ``Cartpole-v1``,
as the overhead of multiprocess or multithread outweighs the environment computation time.
This can also be used for RL methods that
require a vectorized environment, but that you want a single environments to train with.
:param env_fns: a list of functions
that return environments to vectorize
:raises ValueError: If the same environment instance is passed as the output of two or more different env_fn.
"""
actions: np.ndarray
def __init__(self, env_fns: List[Callable[[], gym.Env]]):
self.envs = [_patch_env(fn()) for fn in env_fns]
if len(set([id(env.unwrapped) for env in self.envs])) != len(self.envs):
raise ValueError(
"You tried to create multiple environments, but the function to create them returned the same instance "
"instead of creating different objects. "
"You are probably using `make_vec_env(lambda: env)` or `DummyVecEnv([lambda: env] * n_envs)`. "
"You should replace `lambda: env` by a `make_env` function that "
"creates a new instance of the environment at every call "
"(using `gym.make()` for instance). You can take a look at the documentation for an example. "
"Please read https://github.com/DLR-RM/stable-baselines3/issues/1151 for more information."
)
env = self.envs[0]
super().__init__(len(env_fns), env.observation_space, env.action_space)
obs_space = env.observation_space
self.keys, shapes, dtypes = obs_space_info(obs_space)
self.buf_obs = OrderedDict([(k, np.zeros((self.num_envs, *tuple(shapes[k])), dtype=dtypes[k])) for k in self.keys])
self.buf_dones = np.zeros((self.num_envs,), dtype=bool)
self.buf_rews = np.zeros((self.num_envs,), dtype=np.float32)
self.buf_infos: List[Dict[str, Any]] = [{} for _ in range(self.num_envs)]
self.metadata = env.metadata
[docs] def step_async(self, actions: np.ndarray) -> None:
self.actions = actions
[docs] def step_wait(self) -> VecEnvStepReturn:
# Avoid circular imports
for env_idx in range(self.num_envs):
obs, self.buf_rews[env_idx], terminated, truncated, self.buf_infos[env_idx] = self.envs[env_idx].step(
self.actions[env_idx]
)
# convert to SB3 VecEnv api
self.buf_dones[env_idx] = terminated or truncated
# See https://github.com/openai/gym/issues/3102
# Gym 0.26 introduces a breaking change
self.buf_infos[env_idx]["TimeLimit.truncated"] = truncated and not terminated
if self.buf_dones[env_idx]:
# save final observation where user can get it, then reset
self.buf_infos[env_idx]["terminal_observation"] = obs
obs, self.reset_infos[env_idx] = self.envs[env_idx].reset()
self._save_obs(env_idx, obs)
return (self._obs_from_buf(), np.copy(self.buf_rews), np.copy(self.buf_dones), deepcopy(self.buf_infos))
[docs] def reset(self) -> VecEnvObs:
for env_idx in range(self.num_envs):
maybe_options = {"options": self._options[env_idx]} if self._options[env_idx] else {}
obs, self.reset_infos[env_idx] = self.envs[env_idx].reset(seed=self._seeds[env_idx], **maybe_options)
self._save_obs(env_idx, obs)
# Seeds and options are only used once
self._reset_seeds()
self._reset_options()
return self._obs_from_buf()
[docs] def close(self) -> None:
for env in self.envs:
env.close()
[docs] def get_images(self) -> Sequence[Optional[np.ndarray]]:
if self.render_mode != "rgb_array":
warnings.warn(
f"The render mode is {self.render_mode}, but this method assumes it is `rgb_array` to obtain images."
)
return [None for _ in self.envs]
return [env.render() for env in self.envs] # type: ignore[misc]
[docs] def render(self, mode: Optional[str] = None) -> Optional[np.ndarray]:
"""
Gym environment rendering. If there are multiple environments then
they are tiled together in one image via ``BaseVecEnv.render()``.
:param mode: The rendering type.
"""
return super().render(mode=mode)
def _save_obs(self, env_idx: int, obs: VecEnvObs) -> None:
for key in self.keys:
if key is None:
self.buf_obs[key][env_idx] = obs
else:
self.buf_obs[key][env_idx] = obs[key] # type: ignore[call-overload]
def _obs_from_buf(self) -> VecEnvObs:
return dict_to_obs(self.observation_space, copy_obs_dict(self.buf_obs))
[docs] def get_attr(self, attr_name: str, indices: VecEnvIndices = None) -> List[Any]:
"""Return attribute from vectorized environment (see base class)."""
target_envs = self._get_target_envs(indices)
return [getattr(env_i, attr_name) for env_i in target_envs]
[docs] def set_attr(self, attr_name: str, value: Any, indices: VecEnvIndices = None) -> None:
"""Set attribute inside vectorized environments (see base class)."""
target_envs = self._get_target_envs(indices)
for env_i in target_envs:
setattr(env_i, attr_name, value)
[docs] def env_method(self, method_name: str, *method_args, indices: VecEnvIndices = None, **method_kwargs) -> List[Any]:
"""Call instance methods of vectorized environments."""
target_envs = self._get_target_envs(indices)
return [getattr(env_i, method_name)(*method_args, **method_kwargs) for env_i in target_envs]
[docs] def env_is_wrapped(self, wrapper_class: Type[gym.Wrapper], indices: VecEnvIndices = None) -> List[bool]:
"""Check if worker environments are wrapped with a given wrapper"""
target_envs = self._get_target_envs(indices)
# Import here to avoid a circular import
from stable_baselines3.common import env_util
return [env_util.is_wrapped(env_i, wrapper_class) for env_i in target_envs]
def _get_target_envs(self, indices: VecEnvIndices) -> List[gym.Env]:
indices = self._get_indices(indices)
return [self.envs[i] for i in indices]