| import multiprocessing as mp |
|
|
| import numpy as np |
| from .vec_env import VecEnv, CloudpickleWrapper, clear_mpi_env_vars |
|
|
|
|
| def worker(remote, parent_remote, env_fn_wrappers): |
| def step_env(env, action): |
| ob, reward, done, info = env.step(action) |
| if done: |
| ob = env.reset() |
| return ob, reward, done, info |
|
|
| parent_remote.close() |
| envs = [env_fn_wrapper() for env_fn_wrapper in env_fn_wrappers.x] |
| try: |
| while True: |
| cmd, data = remote.recv() |
| if cmd == 'step': |
| remote.send([step_env(env, action) for env, action in zip(envs, data)]) |
| elif cmd == 'reset': |
| remote.send([env.reset() for env in envs]) |
| elif cmd == 'render': |
| remote.send([env.render(mode='rgb_array') for env in envs]) |
| elif cmd == 'close': |
| remote.close() |
| break |
| elif cmd == 'get_spaces_spec': |
| remote.send(CloudpickleWrapper((envs[0].observation_space, envs[0].action_space, envs[0].spec))) |
| else: |
| raise NotImplementedError |
| except KeyboardInterrupt: |
| print('SubprocVecEnv worker: got KeyboardInterrupt') |
| finally: |
| for env in envs: |
| env.close() |
|
|
|
|
| class SubprocVecEnv(VecEnv): |
| """ |
| VecEnv that runs multiple environments in parallel in subproceses and communicates with them via pipes. |
| Recommended to use when num_envs > 1 and step() can be a bottleneck. |
| """ |
| def __init__(self, env_fns, spaces=None, context='spawn', in_series=1): |
| """ |
| Arguments: |
| |
| env_fns: iterable of callables - functions that create environments to run in subprocesses. Need to be cloud-pickleable |
| in_series: number of environments to run in series in a single process |
| (e.g. when len(env_fns) == 12 and in_series == 3, it will run 4 processes, each running 3 envs in series) |
| """ |
| self.waiting = False |
| self.closed = False |
| self.in_series = in_series |
| nenvs = len(env_fns) |
| assert nenvs % in_series == 0, "Number of envs must be divisible by number of envs to run in series" |
| self.nremotes = nenvs // in_series |
| env_fns = np.array_split(env_fns, self.nremotes) |
| ctx = mp.get_context(context) |
| self.remotes, self.work_remotes = zip(*[ctx.Pipe() for _ in range(self.nremotes)]) |
| self.ps = [ctx.Process(target=worker, args=(work_remote, remote, CloudpickleWrapper(env_fn))) |
| for (work_remote, remote, env_fn) in zip(self.work_remotes, self.remotes, env_fns)] |
| for p in self.ps: |
| p.daemon = True |
| with clear_mpi_env_vars(): |
| p.start() |
| for remote in self.work_remotes: |
| remote.close() |
|
|
| self.remotes[0].send(('get_spaces_spec', None)) |
| observation_space, action_space, self.spec = self.remotes[0].recv().x |
| self.viewer = None |
| VecEnv.__init__(self, nenvs, observation_space, action_space) |
|
|
| def step_async(self, actions): |
| self._assert_not_closed() |
| actions = np.array_split(actions, self.nremotes) |
| for remote, action in zip(self.remotes, actions): |
| remote.send(('step', action)) |
| self.waiting = True |
|
|
| def step_wait(self): |
| self._assert_not_closed() |
| results = [remote.recv() for remote in self.remotes] |
| results = _flatten_list(results) |
| self.waiting = False |
| obs, rews, dones, infos = zip(*results) |
| return _flatten_obs(obs), np.stack(rews), np.stack(dones), infos |
|
|
| def reset(self): |
| self._assert_not_closed() |
| for remote in self.remotes: |
| remote.send(('reset', None)) |
| obs = [remote.recv() for remote in self.remotes] |
| obs = _flatten_list(obs) |
| return _flatten_obs(obs) |
|
|
| def close_extras(self): |
| self.closed = True |
| if self.waiting: |
| for remote in self.remotes: |
| remote.recv() |
| for remote in self.remotes: |
| remote.send(('close', None)) |
| for p in self.ps: |
| p.join() |
|
|
| def get_images(self): |
| self._assert_not_closed() |
| for pipe in self.remotes: |
| pipe.send(('render', None)) |
| imgs = [pipe.recv() for pipe in self.remotes] |
| imgs = _flatten_list(imgs) |
| return imgs |
|
|
| def _assert_not_closed(self): |
| assert not self.closed, "Trying to operate on a SubprocVecEnv after calling close()" |
|
|
| def __del__(self): |
| if not self.closed: |
| self.close() |
|
|
| def _flatten_obs(obs): |
| assert isinstance(obs, (list, tuple)) |
| assert len(obs) > 0 |
|
|
| if isinstance(obs[0], dict): |
| keys = obs[0].keys() |
| return {k: np.stack([o[k] for o in obs]) for k in keys} |
| else: |
| return np.stack(obs) |
|
|
| def _flatten_list(l): |
| assert isinstance(l, (list, tuple)) |
| assert len(l) > 0 |
| assert all([len(l_) > 0 for l_ in l]) |
|
|
| return [l__ for l_ in l for l__ in l_] |
|
|
