Source code for tianshou.env.worker.ray

from typing import Any, Callable, List, Optional, Tuple

import gym
import numpy as np

from tianshou.env.worker import EnvWorker

try:
    import ray
except ImportError:
    pass


class _SetAttrWrapper(gym.Wrapper):

    def set_env_attr(self, key: str, value: Any) -> None:
        setattr(self.env, key, value)

    def get_env_attr(self, key: str) -> Any:
        return getattr(self.env, key)


class RayEnvWorker(EnvWorker):
    """Ray worker used in RayVectorEnv."""

    def __init__(self, env_fn: Callable[[], gym.Env]) -> None:
        self.env = ray.remote(_SetAttrWrapper).options(num_cpus=0).remote(env_fn())
        super().__init__(env_fn)

    def get_env_attr(self, key: str) -> Any:
        return ray.get(self.env.get_env_attr.remote(key))

    def set_env_attr(self, key: str, value: Any) -> None:
        ray.get(self.env.set_env_attr.remote(key, value))

    def reset(self) -> Any:
        return ray.get(self.env.reset.remote())

    @staticmethod
    def wait(  # type: ignore
        workers: List["RayEnvWorker"], wait_num: int, timeout: Optional[float] = None
    ) -> List["RayEnvWorker"]:
        results = [x.result for x in workers]
        ready_results, _ = ray.wait(results, num_returns=wait_num, timeout=timeout)
        return [workers[results.index(result)] for result in ready_results]

    def send_action(self, action: np.ndarray) -> None:
        # self.action is actually a handle
        self.result = self.env.step.remote(action)

    def get_result(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
        return ray.get(self.result)

    def seed(self, seed: Optional[int] = None) -> List[int]:
        super().seed(seed)
        return ray.get(self.env.seed.remote(seed))

    def render(self, **kwargs: Any) -> Any:
        return ray.get(self.env.render.remote(**kwargs))

    def close_env(self) -> None:
        ray.get(self.env.close.remote())