4f65b131aa
Closes: #1058 ### Api Extensions - Batch received two new methods: `to_dict` and `to_list_of_dicts`. #1063 - `Collector`s can now be closed, and their reset is more granular. #1063 - Trainers can control whether collectors should be reset prior to training. #1063 - Convenience constructor for `CollectStats` called `with_autogenerated_stats`. #1063 ### Internal Improvements - `Collector`s rely less on state, the few stateful things are stored explicitly instead of through a `.data` attribute. #1063 - Introduced a first iteration of a naming convention for vars in `Collector`s. #1063 - Generally improved readability of Collector code and associated tests (still quite some way to go). #1063 - Improved typing for `exploration_noise` and within Collector. #1063 ### Breaking Changes - Removed `.data` attribute from `Collector` and its child classes. #1063 - Collectors no longer reset the environment on initialization. Instead, the user might have to call `reset` expicitly or pass `reset_before_collect=True` . #1063 - VectorEnvs now return an array of info-dicts on reset instead of a list. #1063 - Fixed `iter(Batch(...)` which now behaves the same way as `Batch(...).__iter__()`. Can be considered a bugfix. #1063 --------- Co-authored-by: Michael Panchenko <m.panchenko@appliedai.de>
121 lines
3.8 KiB
Python
121 lines
3.8 KiB
Python
from typing import Any
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import numpy as np
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import torch
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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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# Note: No super call because this is a wrapper with overridden __getattribute__
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# It's not a "true" subclass of BaseVectorEnv but it does extend its interface, so
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# it can be used as a drop-in replacement
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# noinspection PyMissingConstructor
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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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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: int | list[int] | np.ndarray | None = 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: int | list[int] | np.ndarray | None = 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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env_id: int | list[int] | np.ndarray | None = None,
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**kwargs: Any,
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) -> tuple[np.ndarray, np.ndarray]:
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return self.venv.reset(env_id, **kwargs)
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def step(
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self,
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action: np.ndarray | torch.Tensor | None,
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id: int | list[int] | np.ndarray | None = 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(self, seed: int | list[int] | None = None) -> list[list[int] | None]:
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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 update_obs_rms: whether to update obs_rms. Default to True.
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"""
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def __init__(self, venv: BaseVectorEnv, update_obs_rms: bool = True) -> 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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env_id: int | list[int] | np.ndarray | None = None,
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**kwargs: Any,
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) -> tuple[np.ndarray, np.ndarray]:
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obs, info = self.venv.reset(env_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 | torch.Tensor | None,
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id: int | list[int] | np.ndarray | None = 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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