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__":`)
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)
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)
def __init__(self, args):
self.env = PepperRLEnv(args)
# Assumes merged lidar
# no direct obstacle positions
from gym.spaces.box import Box
self.observation_space = Box(
low=-100.,
high=100.,
shape=(self.env.kObsBufferSize,
self.env.kStateSize + self.env.kMergedScanSize),
dtype=np.float32,
)
VecEnv.__init__(self, self.env.n_agents(), self.observation_space,
self.env.action_space)
self.keys, shapes, dtypes = obs_space_info(self.observation_space)
self.buf_obs = OrderedDict([(k,
np.zeros(
(self.num_envs, ) + tuple(shapes[k]),
dtype=dtypes[k])) for k in self.keys])
self.buf_dones = np.zeros((self.num_envs, ), dtype=np.bool)
self.buf_rews = np.zeros((self.num_envs, ), dtype=np.float32)
self.buf_infos = [{} for _ in range(self.num_envs)]
self.actions = None
self.metadata = self.env.metadata
def __init__(self, args):
self.envs = [None for _ in range(args.n_envs)]
map2d = None
tsdf = None
for env_idx in range(args.n_envs):
self.envs[env_idx] = PepperRLEnv(args, map_=map2d, tsdf_=tsdf)
map2d = self.envs[env_idx].map2d
tsdf = self.envs[env_idx].tsdf
# Assumes merged lidar
# no direct obstacle positions
from gym.spaces.box import Box
self.observation_space = Box(
low=-100.,
high=100.,
shape=(self.envs[0].kObsBufferSize,
self.envs[0].kStateSize + self.envs[0].kMergedScanSize),
dtype=np.float32,
)
VecEnv.__init__(self, len(self.envs), self.observation_space,
self.envs[0].action_space)
self.keys, shapes, dtypes = obs_space_info(self.observation_space)
self.buf_obs = OrderedDict([(k,
np.zeros(
(self.num_envs, ) + tuple(shapes[k]),
dtype=dtypes[k])) for k in self.keys])
self.buf_dones = np.zeros((self.num_envs, ), dtype=np.bool)
self.buf_rews = np.zeros((self.num_envs, ), dtype=np.float32)
self.buf_infos = [{} for _ in range(self.num_envs)]
self.actions = None
self.metadata = [env.metadata for env in self.envs]
def __init__(self, env_fns):
self.envs = [fn() for fn in env_fns]
env = self.envs[0]
VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
obs_space = env.observation_space
self.keys, shapes, dtypes = obs_space_info(obs_space)
self.buf_obs = OrderedDict([
(k, np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]))
for k in self.keys])
self.buf_dones = np.zeros((self.num_envs,), dtype=np.bool)
self.buf_rews = np.zeros((self.num_envs,), dtype=np.float32)
self.buf_infos = [{} for _ in range(self.num_envs)]
self.actions = None
self.metadata = env.metadata
def __init__(self, *args):
self.env = MultiIARLEnv(*args)
VecEnv.__init__(self, self.env.n_envs, self.env.observation_space,
self.env.action_space)
obs_space = self.env.observation_space
self.keys, shapes, dtypes = obs_space_info(obs_space)
self.buf_obs = OrderedDict([(k,
np.zeros(
(self.num_envs, ) + tuple(shapes[k]),
dtype=dtypes[k])) for k in self.keys])
self.buf_dones = np.zeros((self.num_envs, ), dtype=np.bool)
self.buf_rews = np.zeros((self.num_envs, ), dtype=np.float32)
self.buf_infos = [{} for _ in range(self.num_envs)]
self.actions = None
self.metadata = self.env.metadata
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)
