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)
self.actions = None
self.reset_kwargs = dict()
def __init__(self, env_fns):
self.envs = [fn() for fn in env_fns]
env = self.envs[0]
# self.env = env
VecEnv.__init__(self, len(env_fns), env.observation_space,
env.action_space)
self.actions = None
def __init__(self, env_fns, spaces=None):
"""
Arguments:
env_fns: iterable of callables - functions that create environments to run in subprocesses.
Need to be cloud-pickleable
"""
self.waiting = False
self.closed = False
nenvs = len(env_fns)
self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
self.ps = [
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 # if the main process crashes, we should not cause things to hang
p.start()
for remote in self.work_remotes:
remote.close()
self.remotes[0].send(('get_spaces', None))
observation_space, action_space = self.remotes[0].recv()
self.viewer = None
self.specs = [f().spec for f in env_fns]
VecEnv.__init__(self, len(env_fns), observation_space, action_space)
def __init__(self, env_fns, dis_level):
self.envs = [fn() for fn in env_fns]
env = self.envs[0]
VecEnv.__init__(
self, len(env_fns), env.observation_space, env.action_space
) # adding the key 'episodic_return' because of VecEnv from the module "baselins"
self.actions = None
if dis_level == -1: # test mode, to test panda with only 2 dof
action_range = np.linspace(-2.0, 2.0, 11)
self.action_space = list(itertools.product(action_range, repeat=2))
elif dis_level:
# print("Action space: ", env.action_space.shape[0], type(env.action_space))
action_range = np.linspace(-2.0, 2.0, dis_level)
self.action_space = list(
itertools.product(action_range,
repeat=env.action_space.shape[0]))