Tianshou/tianshou/env/venvs.py

import gym
import warnings
import numpy as np
from typing import List, Union, Optional, Callable, Any

from tianshou.env.worker import EnvWorker, DummyEnvWorker, SubprocEnvWorker, \
    RayEnvWorker


class BaseVectorEnv(gym.Env):
    """Base class for vectorized environments wrapper.

    Usage:
    ::

        env_num = 8
        envs = DummyVectorEnv([lambda: gym.make(task) for _ in range(env_num)])
        assert len(envs) == env_num

    It accepts a list of environment generators. In other words, an environment
    generator ``efn`` of a specific task means that ``efn()`` returns the
    environment of the given task, for example, ``gym.make(task)``.

    All of the VectorEnv must inherit :class:`~tianshou.env.BaseVectorEnv`.
    Here are some other usages:
    ::

        envs.seed(2)  # which is equal to the next line
        envs.seed([2, 3, 4, 5, 6, 7, 8, 9])  # set specific seed for each env
        obs = envs.reset()  # reset all environments
        obs = envs.reset([0, 5, 7])  # reset 3 specific environments
        obs, rew, done, info = envs.step([1] * 8)  # step synchronously
        envs.render()  # render all environments
        envs.close()  # close all environments

    .. warning::

        If you use your own environment, please make sure the ``seed`` method
        is set up properly, e.g.,
        ::

            def seed(self, seed):
                np.random.seed(seed)

        Otherwise, the outputs of these envs may be the same with each other.

    :param env_fns: a list of callable envs, ``env_fns[i]()`` generates the ith
        env.
    :param worker_fn: a callable worker, ``worker_fn(env_fns[i])`` generates a
        worker which contains the i-th env.
    :param int wait_num: use in asynchronous simulation if the time cost of
        ``env.step`` varies with time and synchronously waiting for all
        environments to finish a step is time-wasting. In that case, we can
        return when ``wait_num`` environments finish a step and keep on
        simulation in these environments. If ``None``, asynchronous simulation
        is disabled; else, ``1 <= wait_num <= env_num``.
    :param float timeout: use in asynchronous simulation same as above, in each
        vectorized step it only deal with those environments spending time
        within ``timeout`` seconds.
    """

    def __init__(self,
                 env_fns: List[Callable[[], gym.Env]],
                 worker_fn: Callable[[Callable[[], gym.Env]], EnvWorker],
                 wait_num: Optional[int] = None,
                 timeout: Optional[float] = None,
                 ) -> None:
        self._env_fns = env_fns
        # A VectorEnv contains a pool of EnvWorkers, which corresponds to
        # interact with the given envs (one worker <-> one env).
        self.workers = [worker_fn(fn) for fn in env_fns]
        self.worker_class = type(self.workers[0])
        assert issubclass(self.worker_class, EnvWorker)
        assert all([isinstance(w, self.worker_class) for w in self.workers])

        self.env_num = len(env_fns)
        self.wait_num = wait_num or len(env_fns)
        assert 1 <= self.wait_num <= len(env_fns), \
            f'wait_num should be in [1, {len(env_fns)}], but got {wait_num}'
        self.timeout = timeout
        assert self.timeout is None or self.timeout > 0, \
            f'timeout is {timeout}, it should be positive if provided!'
        self.is_async = self.wait_num != len(env_fns) or timeout is not None
        self.waiting_conn = []
        # environments in self.ready_id is actually ready
        # but environments in self.waiting_id are just waiting when checked,
        # and they may be ready now, but this is not known until we check it
        # in the step() function
        self.waiting_id = []
        # all environments are ready in the beginning
        self.ready_id = list(range(self.env_num))
        self.is_closed = False

    def _assert_is_not_closed(self) -> None:
        assert not self.is_closed, f"Methods of {self.__class__.__name__} "\
            "should not be called after close."

    def __len__(self) -> int:
        """Return len(self), which is the number of environments."""
        return self.env_num

    def __getattribute__(self, key: str) -> Any:
        """Switch the attribute getter depending on the key.

        Any class who inherits ``gym.Env`` will inherit some attributes, like
        ``action_space``. However, we would like the attribute lookup to go
        straight into the worker (in fact, this vector env's action_space is
        always None).
        """
        if key in ['metadata', 'reward_range', 'spec', 'action_space',
                   'observation_space']:  # reserved keys in gym.Env
            return self.__getattr__(key)
        else:
            return super().__getattribute__(key)

    def __getattr__(self, key: str) -> Any:
        """Fetch a list of env attributes.

        This function tries to retrieve an attribute from each individual
        wrapped environment, if it does not belong to the wrapping vector
        environment class.
        """
        return [getattr(worker, key) for worker in self.workers]

    def _wrap_id(self, id: Optional[Union[int, List[int], np.ndarray]] = None
                 ) -> List[int]:
        if id is None:
            id = list(range(self.env_num))
        elif np.isscalar(id):
            id = [id]
        return id

    def _assert_id(self, id: List[int]) -> None:
        for i in id:
            assert i not in self.waiting_id, \
                f'Cannot interact with environment {i} which is stepping now.'
            assert i in self.ready_id, \
                f'Can only interact with ready environments {self.ready_id}.'

    def reset(self, id: Optional[Union[int, List[int], np.ndarray]] = None
              ) -> np.ndarray:
        """Reset the state of some envs and return initial observations.

        If id is None, reset the state of all the environments and return
        initial observations, otherwise reset the specific environments with
        the given id, either an int or a list.
        """
        self._assert_is_not_closed()
        id = self._wrap_id(id)
        if self.is_async:
            self._assert_id(id)
        obs = np.stack([self.workers[i].reset() for i in id])
        return obs

    def step(self,
             action: np.ndarray,
             id: Optional[Union[int, List[int], np.ndarray]] = None
             ) -> List[np.ndarray]:
        """Run one timestep of some environments' dynamics.

        If id is None, run one timestep of all the environments’ dynamics;
        otherwise run one timestep for some environments with given id,  either
        an int or a list. When the end of episode is reached, you are
        responsible for calling reset(id) to reset this environment’s state.

        Accept a batch of action and return a tuple (batch_obs, batch_rew,
        batch_done, batch_info) in numpy format.

        :param numpy.ndarray action: a batch of action provided by the agent.

        :return: A tuple including four items:

            * ``obs`` a numpy.ndarray, the agent's observation of current \
                environments
            * ``rew`` a numpy.ndarray, the amount of rewards returned after \
                previous actions
            * ``done`` a numpy.ndarray, whether these episodes have ended, in \
                which case further step() calls will return undefined results
            * ``info`` a numpy.ndarray, contains auxiliary diagnostic \
                information (helpful for debugging, and sometimes learning)

        For the async simulation:

        Provide the given action to the environments. The action sequence
        should correspond to the ``id`` argument, and the ``id`` argument
        should be a subset of the ``env_id`` in the last returned ``info``
        (initially they are env_ids of all the environments). If action is
        None, fetch unfinished step() calls instead.
        """
        self._assert_is_not_closed()
        id = self._wrap_id(id)
        if not self.is_async:
            assert len(action) == len(id)
            for i, j in enumerate(id):
                self.workers[j].send_action(action[i])
            result = []
            for j in id:
                obs, rew, done, info = self.workers[j].get_result()
                info["env_id"] = j
                result.append((obs, rew, done, info))
        else:
            if action is not None:
                self._assert_id(id)
                assert len(action) == len(id)
                for i, (act, env_id) in enumerate(zip(action, id)):
                    self.workers[env_id].send_action(act)
                    self.waiting_conn.append(self.workers[env_id])
                    self.waiting_id.append(env_id)
                self.ready_id = [x for x in self.ready_id if x not in id]
            ready_conns, result = [], []
            while not ready_conns:
                ready_conns = self.worker_class.wait(
                    self.waiting_conn, self.wait_num, self.timeout)
            for conn in ready_conns:
                waiting_index = self.waiting_conn.index(conn)
                self.waiting_conn.pop(waiting_index)
                env_id = self.waiting_id.pop(waiting_index)
                obs, rew, done, info = conn.get_result()
                info["env_id"] = env_id
                result.append((obs, rew, done, info))
                self.ready_id.append(env_id)
        return list(map(np.stack, zip(*result)))

    def seed(self,
             seed: Optional[Union[int, List[int]]] = None) -> List[List[int]]:
        """Set the seed for all environments.

        Accept ``None``, an int (which will extend ``i`` to
        ``[i, i + 1, i + 2, ...]``) or a list.

        :return: The list of seeds used in this env's random number generators.
            The first value in the list should be the "main" seed, or the value
            which a reproducer pass to "seed".
        """
        self._assert_is_not_closed()
        if seed is None:
            seed = [seed] * self.env_num
        elif np.isscalar(seed):
            seed = [seed + i for i in range(self.env_num)]
        return [w.seed(s) for w, s in zip(self.workers, seed)]

    def render(self, **kwargs) -> List[Any]:
        """Render all of the environments."""
        self._assert_is_not_closed()
        if self.is_async and len(self.waiting_id) > 0:
            raise RuntimeError(
                f"Environments {self.waiting_id} are still "
                f"stepping, cannot render them now.")
        return [w.render(**kwargs) for w in self.workers]

    def close(self) -> None:
        """Close all of the environments.

        This function will be called only once (if not, it will be called
        during garbage collected). This way, ``close`` of all workers can be
        assured.
        """
        self._assert_is_not_closed()
        for w in self.workers:
            w.close()
        self.is_closed = True

    def __del__(self) -> None:
        """Redirect to self.close()."""
        if not self.is_closed:
            self.close()


class DummyVectorEnv(BaseVectorEnv):
    """Dummy vectorized environment wrapper, implemented in for-loop.

    .. seealso::

        Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
        explanation.
    """

    def __init__(self, env_fns: List[Callable[[], gym.Env]],
                 wait_num: Optional[int] = None,
                 timeout: Optional[float] = None) -> None:
        super().__init__(env_fns, DummyEnvWorker,
                         wait_num=wait_num, timeout=timeout)


class VectorEnv(DummyVectorEnv):
    """VectorEnv is renamed to DummyVectorEnv."""

    def __init__(self, *args, **kwargs) -> None:
        warnings.warn(
            'VectorEnv is renamed to DummyVectorEnv, and will be removed in '
            '0.3. Use DummyVectorEnv instead!', Warning)
        super().__init__(*args, **kwargs)


class SubprocVectorEnv(BaseVectorEnv):
    """Vectorized environment wrapper based on subprocess.

    .. seealso::

        Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
        explanation.
    """

    def __init__(self, env_fns: List[Callable[[], gym.Env]],
                 wait_num: Optional[int] = None,
                 timeout: Optional[float] = None) -> None:
        def worker_fn(fn):
            return SubprocEnvWorker(fn, share_memory=False)
        super().__init__(env_fns, worker_fn,
                         wait_num=wait_num, timeout=timeout)


class ShmemVectorEnv(BaseVectorEnv):
    """Optimized SubprocVectorEnv with shared buffers to exchange observations.

    ShmemVectorEnv has exactly the same API as SubprocVectorEnv.

    .. seealso::

        Please refer to :class:`~tianshou.env.SubprocVectorEnv` for more
        detailed explanation.
    """

    def __init__(self, env_fns: List[Callable[[], gym.Env]],
                 wait_num: Optional[int] = None,
                 timeout: Optional[float] = None) -> None:
        def worker_fn(fn):
            return SubprocEnvWorker(fn, share_memory=True)
        super().__init__(env_fns, worker_fn,
                         wait_num=wait_num, timeout=timeout)


class RayVectorEnv(BaseVectorEnv):
    """Vectorized environment wrapper based on ray.

    This is a choice to run distributed environments in a cluster.

    .. seealso::

        Please refer to :class:`~tianshou.env.BaseVectorEnv` for more detailed
        explanation.
    """

    def __init__(self, env_fns: List[Callable[[], gym.Env]],
                 wait_num: Optional[int] = None,
                 timeout: Optional[float] = None) -> None:
        try:
            import ray
        except ImportError as e:
            raise ImportError(
                'Please install ray to support RayVectorEnv: pip install ray'
            ) from e
        if not ray.is_initialized():
            ray.init()
        super().__init__(env_fns, RayEnvWorker,
                         wait_num=wait_num, timeout=timeout)