from typing import Any, List, Optional, Tuple, Union
import numpy as np
from tianshou.env.venvs import GYM_RESERVED_KEYS, BaseVectorEnv
from tianshou.utils import RunningMeanStd
[docs]class VectorEnvWrapper(BaseVectorEnv):
"""Base class for vectorized environments wrapper."""
def __init__(self, venv: BaseVectorEnv) -> None:
self.venv = venv
self.is_async = venv.is_async
[docs] def __len__(self) -> int:
return len(self.venv)
def __getattribute__(self, key: str) -> Any:
if key in GYM_RESERVED_KEYS: # reserved keys in gym.Env
return getattr(self.venv, key)
else:
return super().__getattribute__(key)
[docs] def get_env_attr(
self,
key: str,
id: Optional[Union[int, List[int], np.ndarray]] = None,
) -> List[Any]:
return self.venv.get_env_attr(key, id)
[docs] def set_env_attr(
self,
key: str,
value: Any,
id: Optional[Union[int, List[int], np.ndarray]] = None,
) -> None:
return self.venv.set_env_attr(key, value, id)
[docs] def reset(
self,
id: Optional[Union[int, List[int], np.ndarray]] = None,
**kwargs: Any,
) -> Union[np.ndarray, Tuple[np.ndarray, List[dict]]]:
return self.venv.reset(id, **kwargs)
[docs] def step(
self,
action: np.ndarray,
id: Optional[Union[int, List[int], np.ndarray]] = None,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
return self.venv.step(action, id)
[docs] def seed(
self,
seed: Optional[Union[int, List[int]]] = None,
) -> List[Optional[List[int]]]:
return self.venv.seed(seed)
[docs] def render(self, **kwargs: Any) -> List[Any]:
return self.venv.render(**kwargs)
[docs] def close(self) -> None:
self.venv.close()
[docs]class VectorEnvNormObs(VectorEnvWrapper):
"""An observation normalization wrapper for vectorized environments.
:param bool update_obs_rms: whether to update obs_rms. Default to True.
:param float clip_obs: the maximum absolute value for observation. Default to
10.0.
:param float epsilon: To avoid division by zero.
"""
def __init__(
self,
venv: BaseVectorEnv,
update_obs_rms: bool = True,
clip_obs: float = 10.0,
epsilon: float = np.finfo(np.float32).eps.item(),
) -> None:
super().__init__(venv)
# initialize observation running mean/std
self.update_obs_rms = update_obs_rms
self.obs_rms = RunningMeanStd()
self.clip_max = clip_obs
self.eps = epsilon
[docs] def reset(
self,
id: Optional[Union[int, List[int], np.ndarray]] = None,
**kwargs: Any,
) -> Union[np.ndarray, Tuple[np.ndarray, List[dict]]]:
retval = self.venv.reset(id, **kwargs)
reset_returns_info = isinstance(
retval, (tuple, list)
) and len(retval) == 2 and isinstance(retval[1], dict)
if reset_returns_info:
obs, info = retval
else:
obs = retval
if isinstance(obs, tuple):
raise TypeError(
"Tuple observation space is not supported. ",
"Please change it to array or dict space",
)
if self.obs_rms and self.update_obs_rms:
self.obs_rms.update(obs)
obs = self._norm_obs(obs)
if reset_returns_info:
return obs, info
else:
return obs
[docs] def step(
self,
action: np.ndarray,
id: Optional[Union[int, List[int], np.ndarray]] = None,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
obs, rew, done, info = self.venv.step(action, id)
if self.obs_rms and self.update_obs_rms:
self.obs_rms.update(obs)
return self._norm_obs(obs), rew, done, info
def _norm_obs(self, obs: np.ndarray) -> np.ndarray:
if self.obs_rms:
obs = (obs - self.obs_rms.mean) / np.sqrt(self.obs_rms.var + self.eps)
obs = np.clip(obs, -self.clip_max, self.clip_max)
return obs
[docs] def set_obs_rms(self, obs_rms: RunningMeanStd) -> None:
"""Set with given observation running mean/std."""
self.obs_rms = obs_rms
[docs] def get_obs_rms(self) -> RunningMeanStd:
"""Return observation running mean/std."""
return self.obs_rms