6c6c872523
Changes: - Disclaimer in README - Replaced all occurences of Gym with Gymnasium - Removed code that is now dead since we no longer need to support the old step API - Updated type hints to only allow new step API - Increased required version of envpool to support Gymnasium - Increased required version of PettingZoo to support Gymnasium - Updated `PettingZooEnv` to only use the new step API, removed hack to also support old API - I had to add some `# type: ignore` comments, due to new type hinting in Gymnasium. I'm not that familiar with type hinting but I believe that the issue is on the Gymnasium side and we are looking into it. - Had to update `MyTestEnv` to support `options` kwarg - Skip NNI tests because they still use OpenAI Gym - Also allow `PettingZooEnv` in vector environment - Updated doc page about ReplayBuffer to also talk about terminated and truncated flags. Still need to do: - Update the Jupyter notebooks in docs - Check the entire code base for more dead code (from compatibility stuff) - Check the reset functions of all environments/wrappers in code base to make sure they use the `options` kwarg - Someone might want to check test_env_finite.py - Is it okay to allow `PettingZooEnv` in vector environments? Might need to update docs?
124 lines
3.6 KiB
Python
124 lines
3.6 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.utils import gym_new_venv_step_type
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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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def reset(
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self,
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id: Optional[Union[int, List[int], np.ndarray]] = None,
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**kwargs: Any,
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) -> Tuple[np.ndarray, Union[dict, List[dict]]]:
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return self.venv.reset(id, **kwargs)
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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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) -> gym_new_venv_step_type:
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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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"""
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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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) -> 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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def reset(
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self,
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id: Optional[Union[int, List[int], np.ndarray]] = None,
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**kwargs: Any,
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) -> Tuple[np.ndarray, Union[dict, List[dict]]]:
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obs, info = self.venv.reset(id, **kwargs)
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if isinstance(obs, tuple): # type: ignore
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raise TypeError(
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"Tuple observation space is not supported. ",
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"Please change it to array or dict space",
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)
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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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obs = self._norm_obs(obs)
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return obs, info
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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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) -> gym_new_venv_step_type:
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step_results = 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(step_results[0])
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return (self._norm_obs(step_results[0]), *step_results[1:])
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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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return self.obs_rms.norm(obs) # type: ignore
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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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