Source code for stable_baselines.common.vec_env.subproc_vec_env

import os
import multiprocessing
from collections import OrderedDict
from typing import Sequence

import gym
import numpy as np

from stable_baselines.common.vec_env.base_vec_env import VecEnv, CloudpickleWrapper

def _worker(remote, parent_remote, env_fn_wrapper):
    env = env_fn_wrapper.var()
    while True:
            cmd, data = remote.recv()
            if cmd == 'step':
                observation, reward, done, info = env.step(data)
                if done:
                    # save final observation where user can get it, then reset
                    info['terminal_observation'] = observation
                    observation = env.reset()
                remote.send((observation, reward, done, info))
            elif cmd == 'seed':
            elif cmd == 'reset':
                observation = env.reset()
            elif cmd == 'render':
            elif cmd == 'close':
            elif cmd == 'get_spaces':
                remote.send((env.observation_space, env.action_space))
            elif cmd == 'env_method':
                method = getattr(env, data[0])
                remote.send(method(*data[1], **data[2]))
            elif cmd == 'get_attr':
                remote.send(getattr(env, data))
            elif cmd == 'set_attr':
                remote.send(setattr(env, data[0], data[1]))
                raise NotImplementedError("`{}` is not implemented in the worker".format(cmd))
        except EOFError:

[docs]class SubprocVecEnv(VecEnv): """ Creates a multiprocess vectorized wrapper for multiple environments, distributing each environment to its own process, allowing significant speed up when the environment is computationally complex. For performance reasons, if your environment is not IO bound, the number of environments should not exceed the number of logical cores on your CPU. .. warning:: Only 'forkserver' and 'spawn' start methods are thread-safe, which is important when TensorFlow sessions or other non thread-safe libraries are used in the parent (see issue #217). However, compared to 'fork' they incur a small start-up cost and have restrictions on global variables. With those methods, users must wrap the code in an ``if __name__ == "__main__":`` block. For more information, see the multiprocessing documentation. :param env_fns: ([callable]) A list of functions that will create the environments (each callable returns a `Gym.Env` instance when called). :param start_method: (str) method used to start the subprocesses. Must be one of the methods returned by multiprocessing.get_all_start_methods(). Defaults to 'forkserver' on available platforms, and 'spawn' otherwise. """ def __init__(self, env_fns, start_method=None): self.waiting = False self.closed = False n_envs = len(env_fns) # In some cases (like on GitHub workflow machine when running tests), # "forkserver" method results in an "connection error" (probably due to mpi) # We allow to bypass the default start method if an environment variable # is specified by the user if start_method is None: start_method = os.environ.get("DEFAULT_START_METHOD") # No DEFAULT_START_METHOD was specified, start_method may still be None if start_method is None: # Fork is not a thread safe method (see issue #217) # but is more user friendly (does not require to wrap the code in # a `if __name__ == "__main__":`) forkserver_available = 'forkserver' in multiprocessing.get_all_start_methods() start_method = 'forkserver' if forkserver_available else 'spawn' ctx = multiprocessing.get_context(start_method) self.remotes, self.work_remotes = zip(*[ctx.Pipe(duplex=True) for _ in range(n_envs)]) self.processes = [] for work_remote, remote, env_fn in zip(self.work_remotes, self.remotes, env_fns): args = (work_remote, remote, CloudpickleWrapper(env_fn)) # daemon=True: if the main process crashes, we should not cause things to hang process = ctx.Process(target=_worker, args=args, daemon=True) # pytype:disable=attribute-error process.start() self.processes.append(process) work_remote.close() self.remotes[0].send(('get_spaces', None)) observation_space, action_space = self.remotes[0].recv() VecEnv.__init__(self, len(env_fns), observation_space, action_space)
[docs] def step_async(self, actions): for remote, action in zip(self.remotes, actions): remote.send(('step', action)) self.waiting = True
[docs] def step_wait(self): results = [remote.recv() for remote in self.remotes] self.waiting = False obs, rews, dones, infos = zip(*results) return _flatten_obs(obs, self.observation_space), np.stack(rews), np.stack(dones), infos
[docs] def seed(self, seed=None): for idx, remote in enumerate(self.remotes): remote.send(('seed', seed + idx)) return [remote.recv() for remote in self.remotes]
[docs] def reset(self): for remote in self.remotes: remote.send(('reset', None)) obs = [remote.recv() for remote in self.remotes] return _flatten_obs(obs, self.observation_space)
[docs] def close(self): if self.closed: return if self.waiting: for remote in self.remotes: remote.recv() for remote in self.remotes: remote.send(('close', None)) for process in self.processes: process.join() self.closed = True
[docs] def get_images(self) -> Sequence[np.ndarray]: for pipe in self.remotes: # gather images from subprocesses # `mode` will be taken into account later pipe.send(('render', 'rgb_array')) imgs = [pipe.recv() for pipe in self.remotes] return imgs
[docs] def get_attr(self, attr_name, indices=None): """Return attribute from vectorized environment (see base class).""" target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(('get_attr', attr_name)) return [remote.recv() for remote in target_remotes]
[docs] def set_attr(self, attr_name, value, indices=None): """Set attribute inside vectorized environments (see base class).""" target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(('set_attr', (attr_name, value))) for remote in target_remotes: remote.recv()
[docs] def env_method(self, method_name, *method_args, indices=None, **method_kwargs): """Call instance methods of vectorized environments.""" target_remotes = self._get_target_remotes(indices) for remote in target_remotes: remote.send(('env_method', (method_name, method_args, method_kwargs))) return [remote.recv() for remote in target_remotes]
def _get_target_remotes(self, indices): """ Get the connection object needed to communicate with the wanted envs that are in subprocesses. :param indices: (None,int,Iterable) refers to indices of envs. :return: ([multiprocessing.Connection]) Connection object to communicate between processes. """ indices = self._get_indices(indices) return [self.remotes[i] for i in indices]
def _flatten_obs(obs, space): """ Flatten observations, depending on the observation space. :param obs: (list<X> or tuple<X> where X is dict<ndarray>, tuple<ndarray> or ndarray) observations. A list or tuple of observations, one per environment. Each environment observation may be a NumPy array, or a dict or tuple of NumPy arrays. :return (OrderedDict<ndarray>, tuple<ndarray> or ndarray) flattened observations. A flattened NumPy array or an OrderedDict or tuple of flattened numpy arrays. Each NumPy array has the environment index as its first axis. """ assert isinstance(obs, (list, tuple)), "expected list or tuple of observations per environment" assert len(obs) > 0, "need observations from at least one environment" if isinstance(space, gym.spaces.Dict): assert isinstance(space.spaces, OrderedDict), "Dict space must have ordered subspaces" assert isinstance(obs[0], dict), "non-dict observation for environment with Dict observation space" return OrderedDict([(k, np.stack([o[k] for o in obs])) for k in space.spaces.keys()]) elif isinstance(space, gym.spaces.Tuple): assert isinstance(obs[0], tuple), "non-tuple observation for environment with Tuple observation space" obs_len = len(space.spaces) return tuple((np.stack([o[i] for o in obs]) for i in range(obs_len))) else: return np.stack(obs)