import multiprocessing
from collections import OrderedDict
import gym
import numpy as np
from stable_baselines.common.vec_env import VecEnv, CloudpickleWrapper
from stable_baselines.common.tile_images import tile_images
def _worker(remote, parent_remote, env_fn_wrapper):
parent_remote.close()
env = env_fn_wrapper.var()
while True:
try:
cmd, data = remote.recv()
if cmd == 'step':
observation, reward, done, info = env.step(data)
if done:
observation = env.reset()
remote.send((observation, reward, done, info))
elif cmd == 'reset':
observation = env.reset()
remote.send(observation)
elif cmd == 'render':
remote.send(env.render(*data[0], **data[1]))
elif cmd == 'close':
remote.close()
break
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]))
else:
raise NotImplementedError
except EOFError:
break
[docs]class SubprocVecEnv(VecEnv):
"""
Creates a multiprocess vectorized wrapper for multiple environments
.. 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__":``
For more information, see the multiprocessing documentation.
:param env_fns: ([Gym Environment]) Environments to run in subprocesses
: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 'fork' 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)
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__":`)
fork_available = 'fork' in multiprocessing.get_all_start_methods()
start_method = 'fork' if fork_available else 'spawn'
ctx = multiprocessing.get_context(start_method)
self.remotes, self.work_remotes = zip(*[ctx.Pipe() 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)
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 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 render(self, mode='human', *args, **kwargs):
for pipe in self.remotes:
# gather images from subprocesses
# `mode` will be taken into account later
pipe.send(('render', (args, {'mode': 'rgb_array', **kwargs})))
imgs = [pipe.recv() for pipe in self.remotes]
# Create a big image by tiling images from subprocesses
bigimg = tile_images(imgs)
if mode == 'human':
import cv2
cv2.imshow('vecenv', bigimg[:, :, ::-1])
cv2.waitKey(1)
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
[docs] def get_images(self):
for pipe in self.remotes:
pipe.send(('render', {"mode": 'rgb_array'}))
imgs = [pipe.recv() for pipe in self.remotes]
return imgs
[docs] def env_method(self, method_name, *method_args, **method_kwargs):
"""
Provides an interface to call arbitrary class methods of vectorized environments
:param method_name: (str) The name of the env class method to invoke
:param method_args: (tuple) Any positional arguments to provide in the call
:param method_kwargs: (dict) Any keyword arguments to provide in the call
:return: (list) List of items retured by each environment's method call
"""
for remote in self.remotes:
remote.send(('env_method', (method_name, method_args, method_kwargs)))
return [remote.recv() for remote in self.remotes]
[docs] def get_attr(self, attr_name):
"""
Provides a mechanism for getting class attribues from vectorized environments
(note: attribute value returned must be picklable)
:param attr_name: (str) The name of the attribute whose value to return
:return: (list) List of values of 'attr_name' in all environments
"""
for remote in self.remotes:
remote.send(('get_attr', attr_name))
return [remote.recv() for remote in self.remotes]
[docs] def set_attr(self, attr_name, value, indices=None):
"""
Provides a mechanism for setting arbitrary class attributes inside vectorized environments
(note: this is a broadcast of a single value to all instances)
(note: the value must be picklable)
:param attr_name: (str) Name of attribute to assign new value
:param value: (obj) Value to assign to 'attr_name'
:param indices: (list,tuple) Iterable containing indices of envs whose attr to set
:return: (list) in case env access methods might return something, they will be returned in a list
"""
if indices is None:
indices = range(len(self.remotes))
elif isinstance(indices, int):
indices = [indices]
for remote in [self.remotes[i] for i in indices]:
remote.send(('set_attr', (attr_name, value)))
return [remote.recv() for remote in [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)