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?
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
from typing import Any, Callable, List, Optional
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import gymnasium as gym
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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.worker import EnvWorker
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try:
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import ray
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except ImportError:
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pass
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class _SetAttrWrapper(gym.Wrapper):
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def set_env_attr(self, key: str, value: Any) -> None:
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setattr(self.env.unwrapped, key, value)
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def get_env_attr(self, key: str) -> Any:
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return getattr(self.env, key)
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class RayEnvWorker(EnvWorker):
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"""Ray worker used in RayVectorEnv."""
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def __init__(self, env_fn: Callable[[], gym.Env]) -> None:
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self.env = ray.remote(_SetAttrWrapper).options( # type: ignore
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num_cpus=0
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).remote(env_fn())
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super().__init__(env_fn)
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def get_env_attr(self, key: str) -> Any:
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return ray.get(self.env.get_env_attr.remote(key))
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def set_env_attr(self, key: str, value: Any) -> None:
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ray.get(self.env.set_env_attr.remote(key, value))
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def reset(self, **kwargs: Any) -> Any:
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if "seed" in kwargs:
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super().seed(kwargs["seed"])
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return ray.get(self.env.reset.remote(**kwargs))
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@staticmethod
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def wait( # type: ignore
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workers: List["RayEnvWorker"], wait_num: int, timeout: Optional[float] = None
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) -> List["RayEnvWorker"]:
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results = [x.result for x in workers]
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ready_results, _ = ray.wait(results, num_returns=wait_num, timeout=timeout)
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return [workers[results.index(result)] for result in ready_results]
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def send(self, action: Optional[np.ndarray], **kwargs: Any) -> None:
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# self.result is actually a handle
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if action is None:
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self.result = self.env.reset.remote(**kwargs)
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else:
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self.result = self.env.step.remote(action)
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def recv(self) -> gym_new_venv_step_type:
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return ray.get(self.result) # type: ignore
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def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
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super().seed(seed)
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try:
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return ray.get(self.env.seed.remote(seed))
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except (AttributeError, NotImplementedError):
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self.env.reset.remote(seed=seed)
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return None
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def render(self, **kwargs: Any) -> Any:
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return ray.get(self.env.render.remote(**kwargs))
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def close_env(self) -> None:
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ray.get(self.env.close.remote())
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