Source code for tianshou.env.venvs

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

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


[docs]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: List[EnvWorker] = [] # 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: List[int] = [] # 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__} cannot be called after " "close.")
[docs] 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) -> List[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 ) -> Union[List[int], np.ndarray]: 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}."
[docs] 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
[docs] 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: List[EnvWorker] = [] while not ready_conns: ready_conns = self.worker_class.wait( self.waiting_conn, self.wait_num, self.timeout) result = [] 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)))
[docs] def seed( self, seed: Optional[Union[int, List[int]]] = None ) -> List[Optional[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() seed_list: Union[List[None], List[int]] if seed is None: seed_list = [seed] * self.env_num elif isinstance(seed, int): seed_list = [seed + i for i in range(self.env_num)] else: seed_list = seed return [w.seed(s) for w, s in zip(self.workers, seed_list)]
[docs] def render(self, **kwargs: Any) -> 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 stepping, cannot " "render them now.") return [w.render(**kwargs) for w in self.workers]
[docs] 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()
[docs]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)
[docs]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: Callable[[], gym.Env]) -> SubprocEnvWorker: return SubprocEnvWorker(fn, share_memory=False) super().__init__( env_fns, worker_fn, wait_num=wait_num, timeout=timeout)
[docs]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: Callable[[], gym.Env]) -> SubprocEnvWorker: return SubprocEnvWorker(fn, share_memory=True) super().__init__( env_fns, worker_fn, wait_num=wait_num, timeout=timeout)
[docs]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)