Gym-MiniGrid/pytorch_rl/vec_env/__init__.py

from abc import ABC, abstractmethod

class VecEnv(ABC):

    def __init__(self, num_envs, observation_space, action_space):
        self.num_envs = num_envs
        self.observation_space = observation_space
        self.action_space = action_space

    """
    An abstract asynchronous, vectorized environment.
    """
    @abstractmethod
    def reset(self):
        """
        Reset all the environments and return an array of
        observations.

        If step_async is still doing work, that work will
        be cancelled and step_wait() should not be called
        until step_async() is invoked again.
        """
        pass

    @abstractmethod
    def step_async(self, actions):
        """
        Tell all the environments to start taking a step
        with the given actions.
        Call step_wait() to get the results of the step.

        You should not call this if a step_async run is
        already pending.
        """
        pass

    @abstractmethod
    def step_wait(self):
        """
        Wait for the step taken with step_async().

        Returns (obs, rews, dones, infos):
         - obs: an array of observations
         - rews: an array of rewards
         - dones: an array of "episode done" booleans
         - infos: an array of info objects
        """
        pass

    @abstractmethod
    def close(self):
        """
        Clean up the environments' resources.
        """
        pass

    def step(self, actions):
        self.step_async(actions)
        return self.step_wait()

    def render(self):
        logger.warn('Render not defined for %s'%self)

class VecEnvWrapper(VecEnv):
    def __init__(self, venv, observation_space=None, action_space=None):
        self.venv = venv
        VecEnv.__init__(self,
            num_envs=venv.num_envs,
            observation_space=observation_space or venv.observation_space,
            action_space=action_space or venv.action_space)

    def step_async(self, actions):
        self.venv.step_async(actions)

    @abstractmethod
    def reset(self):
        pass

    @abstractmethod
    def step_wait(self):
        pass

    def close(self):
        return self.venv.close()

    def render(self):
        self.venv.render()

class CloudpickleWrapper(object):
    """
    Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
    """
    def __init__(self, x):
        self.x = x
    def __getstate__(self):
        import cloudpickle
        return cloudpickle.dumps(self.x)
    def __setstate__(self, ob):
        import pickle
        self.x = pickle.loads(ob)