2a7c151738
- add VectorEnvWrapper and VectorEnvNormObs - obs_rms store in policy save/load - align mujoco scripts with atari: obs_norm, envpool, wandb and README
121 lines
3.7 KiB
Python
121 lines
3.7 KiB
Python
from typing import Any, List, Optional, Tuple, Union
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import numpy as np
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from tianshou.env.venvs import GYM_RESERVED_KEYS, BaseVectorEnv
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from tianshou.utils import RunningMeanStd
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class VectorEnvWrapper(BaseVectorEnv):
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"""Base class for vectorized environments wrapper."""
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def __init__(self, venv: BaseVectorEnv) -> None:
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self.venv = venv
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self.is_async = venv.is_async
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def __len__(self) -> int:
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return len(self.venv)
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def __getattribute__(self, key: str) -> Any:
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if key in GYM_RESERVED_KEYS: # reserved keys in gym.Env
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return getattr(self.venv, key)
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else:
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return super().__getattribute__(key)
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def get_env_attr(
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self,
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key: str,
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id: Optional[Union[int, List[int], np.ndarray]] = None,
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) -> List[Any]:
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return self.venv.get_env_attr(key, id)
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def set_env_attr(
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self,
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key: str,
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value: Any,
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id: Optional[Union[int, List[int], np.ndarray]] = None,
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) -> None:
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return self.venv.set_env_attr(key, value, id)
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# TODO: compatible issue with reset -> (obs, info)
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def reset(
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self, id: Optional[Union[int, List[int], np.ndarray]] = None
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) -> np.ndarray:
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return self.venv.reset(id)
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def step(
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self,
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action: np.ndarray,
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id: Optional[Union[int, List[int], np.ndarray]] = None,
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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return self.venv.step(action, id)
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def seed(
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self,
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seed: Optional[Union[int, List[int]]] = None,
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) -> List[Optional[List[int]]]:
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return self.venv.seed(seed)
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def render(self, **kwargs: Any) -> List[Any]:
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return self.venv.render(**kwargs)
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def close(self) -> None:
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self.venv.close()
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class VectorEnvNormObs(VectorEnvWrapper):
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"""An observation normalization wrapper for vectorized environments.
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:param bool update_obs_rms: whether to update obs_rms. Default to True.
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:param float clip_obs: the maximum absolute value for observation. Default to
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10.0.
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:param float epsilon: To avoid division by zero.
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"""
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def __init__(
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self,
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venv: BaseVectorEnv,
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update_obs_rms: bool = True,
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clip_obs: float = 10.0,
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epsilon: float = np.finfo(np.float32).eps.item(),
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) -> None:
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super().__init__(venv)
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# initialize observation running mean/std
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self.update_obs_rms = update_obs_rms
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self.obs_rms = RunningMeanStd()
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self.clip_max = clip_obs
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self.eps = epsilon
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# TODO: compatible issue with reset -> (obs, info)
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def reset(
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self, id: Optional[Union[int, List[int], np.ndarray]] = None
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) -> np.ndarray:
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obs = self.venv.reset(id)
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if self.obs_rms and self.update_obs_rms:
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self.obs_rms.update(obs)
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return self._norm_obs(obs)
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def step(
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self,
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action: np.ndarray,
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id: Optional[Union[int, List[int], np.ndarray]] = None,
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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obs, rew, done, info = self.venv.step(action, id)
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if self.obs_rms and self.update_obs_rms:
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self.obs_rms.update(obs)
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return self._norm_obs(obs), rew, done, info
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def _norm_obs(self, obs: np.ndarray) -> np.ndarray:
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if self.obs_rms:
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obs = (obs - self.obs_rms.mean) / np.sqrt(self.obs_rms.var + self.eps)
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obs = np.clip(obs, -self.clip_max, self.clip_max)
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return obs
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def set_obs_rms(self, obs_rms: RunningMeanStd) -> None:
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"""Set with given observation running mean/std."""
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self.obs_rms = obs_rms
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def get_obs_rms(self) -> RunningMeanStd:
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"""Return observation running mean/std."""
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return self.obs_rms
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