def __init__(
self,
env,
reward_scale=1.,
obs_mean=None,
obs_std=None,
):
# self._wrapped_env needs to be called first because
# Serializable.quick_init calls getattr, on this class. And the
# implementation of getattr (see below) calls self._wrapped_env.
# Without setting this first, the call to self._wrapped_env would call
# getattr again (since it's not set yet) and therefore loop forever.
self._wrapped_env = env
# Or else serialization gets delegated to the wrapped_env. Serialize
# this env separately from the wrapped_env.
self._serializable_initialized = False
Serializable.quick_init(self, locals())
ProxyEnv.__init__(self, env)
self._should_normalize = not (obs_mean is None and obs_std is None)
if self._should_normalize:
if obs_mean is None:
obs_mean = np.zeros_like(env.observation_space.low)
else:
obs_mean = np.array(obs_mean)
if obs_std is None:
obs_std = np.ones_like(env.observation_space.low)
else:
obs_std = np.array(obs_std)
self._reward_scale = reward_scale
self._obs_mean = obs_mean
self._obs_std = obs_std
ub = np.ones(self._wrapped_env.action_space.shape)
self.action_space = Box(-1 * ub, ub)
def __init__(
self,
env,
policy,
exploration_policy,
max_path_length,
train_rollout_function,
eval_rollout_function,
num_workers=2,
):
Serializable.quick_init(self, locals())
super().__init__(env)
self.num_workers = num_workers
# Let self.worker_limits[True] be the max number of workers for training
# and self.worker_limits[False] be the max number of workers for eval.
self.worker_limits = {
True: math.ceil(self.num_workers / 2),
False: math.ceil(self.num_workers / 2),
}
self.parent_pipes = []
self.child_pipes = []
for _ in range(num_workers):
parent_conn, child_conn = Pipe()
self.parent_pipes.append(parent_conn)
self.child_pipes.append(child_conn)
self._workers = [
Process(target=RemoteRolloutEnv._worker_loop,
args=(
self.child_pipes[i],
env,
policy,
exploration_policy,
max_path_length,
cloudpickle.dumps(train_rollout_function),
cloudpickle.dumps(eval_rollout_function),
)) for i in range(num_workers)
]
for worker in self._workers:
worker.start()
self.free_pipes = set(self.parent_pipes)
# self.pipe_info[pipe] stores (epoch, train_type)
self.pipe_info = {}
# Let self.promise_list[True] be the promises for training
# and self.promise_list[False] be the promises for eval.
self.rollout_promise_list = {
True: [],
False: [],
}
def __init__(self,
action_space,
max_sigma=1.0,
min_sigma=None,
decay_period=1000000):
assert len(action_space.shape) == 1
Serializable.quick_init(self, locals())
self._max_sigma = max_sigma
if min_sigma is None:
min_sigma = max_sigma
self._min_sigma = min_sigma
self._decay_period = decay_period
self._action_space = action_space
def __init__(self, env):
Serializable.quick_init(self, locals())
ProxyEnv.__init__(self, env)
self.action_space = convert_space_to_tf_space(
self._wrapped_env.action_space
)
self.observation_space = convert_space_to_tf_space(
self._wrapped_env.observation_space
)
from rllab.envs.env_spec import EnvSpec
self.spec = EnvSpec(
observation_space=self.observation_space,
action_space=self.action_space,
)
def save_init_params(self, locals):
"""
Should call this FIRST THING in the __init__ method if you ever want
to serialize or clone this network.
Usage:
```
def __init__(self, ...):
self.init_serialization(locals())
...
```
:param locals:
:return:
"""
Serializable.quick_init(self, locals)
def __init__(self, wrapped_env):
Serializable.quick_init(self, locals())
self._wrapped_env = wrapped_env
self.action_space = self._wrapped_env.action_space
self.observation_space = self._wrapped_env.observation_space
def __init__(self, action_space, prob_random_action=0.1):
Serializable.quick_init(self, locals())
Serializable.quick_init(self, locals())
self.prob_random_action = prob_random_action
self.action_space = action_space
def __init__(self, action_space, low=0., high=1.):
Serializable.quick_init(self, locals())
self._low = action_space.low
self._high = action_space.high
def __init__(self, action_space, sigma=1.0):
assert len(action_space.shape) == 1
Serializable.quick_init(self, locals())
super().__init__()
self._sigma = sigma
self._action_space = action_space