Source code for tianshou.env.worker.ray

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

import gym
import numpy as np

from tianshou.env.utils import gym_new_venv_step_type, gym_old_venv_step_type
from tianshou.env.worker import EnvWorker

try:
    import ray
except ImportError:
    pass


class _SetAttrWrapper(gym.Wrapper):

    def __init__(self, env: gym.Env) -> None:
        """Constructor of this wrapper.

        For Gym 0.25, wrappers will automatically
        change to the old step API. We need to check
        which API ``env`` follows and adjust the
        wrapper accordingly.
        """
        env.reset()
        step_result = env.step(env.action_space.sample())
        new_step_api = len(step_result) == 5
        try:
            super().__init__(env, new_step_api=new_step_api)  # type: ignore
        except TypeError:  # The kwarg `new_step_api` was removed in Gym 0.26
            super().__init__(env)

    def set_env_attr(self, key: str, value: Any) -> None:
        setattr(self.env.unwrapped, 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(  # type: ignore
            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, **kwargs: Any) -> Any:
        if "seed" in kwargs:
            super().seed(kwargs["seed"])
        return ray.get(self.env.reset.remote(**kwargs))

    @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(self, action: Optional[np.ndarray], **kwargs: Any) -> None:
        # self.result is actually a handle
        if action is None:
            self.result = self.env.reset.remote(**kwargs)
        else:
            self.result = self.env.step.remote(action)

    def recv(self) -> Union[gym_old_venv_step_type, gym_new_venv_step_type]:
        return ray.get(self.result)  # type: ignore

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

    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())