def __init__(self, mdp_info, clip_obs=10., alpha=1e-32):
"""
Constructor.
Args:
mdp_info (MDPInfo): information of the MDP;
clip_obs (float, 10.): values to clip the normalized observations;
alpha (float, 1e-32): moving average catchup parameter for the
normalization.
"""
self.clip_obs = clip_obs
self.obs_shape = mdp_info.observation_space.shape
self.obs_runstand = RunningStandardization(shape=self.obs_shape,
alpha=alpha)
class StandardizationPreprocessor(object):
"""
Preprocess observations from the environment using a running
standardization.
"""
def __init__(self, mdp_info, clip_obs=10., alpha=1e-32):
"""
Constructor.
Args:
mdp_info (MDPInfo): information of the MDP;
clip_obs (float, 10.): values to clip the normalized observations;
alpha (float, 1e-32): moving average catchup parameter for the
normalization.
"""
self.clip_obs = clip_obs
self.obs_shape = mdp_info.observation_space.shape
self.obs_runstand = RunningStandardization(shape=self.obs_shape,
alpha=alpha)
def __call__(self, obs):
"""
Call function to normalize the observation.
Args:
obs (np.ndarray): observation to be normalized.
Returns:
Normalized observation array with the same shape.
"""
assert obs.shape == self.obs_shape, \
"Values given to running_norm have incorrect shape " \
"(obs shape: {}, expected shape: {})" \
.format(obs.shape, self.obs_shape)
self.obs_runstand.update_stats(obs)
norm_obs = np.clip(
(obs - self.obs_runstand.mean) / self.obs_runstand.std,
-self.clip_obs, self.clip_obs)
return norm_obs
def get_state(self):
"""
Returns:
A dictionary with the normalization state.
"""
return self.obs_runstand.get_state()
def set_state(self, data):
"""
Set the current normalization state from the data dict.
"""
self.obs_runstand.set_state(data)
def save_state(self, path):
"""
Save the running normalization state to path.
Args:
path (str): path to save the running normalization state.
"""
with open(path, 'wb') as f:
pickle.dump(self.get_state(), f, protocol=3)
def load_state(self, path):
"""
Load the running normalization state from path.
Args:
path (string): path to load the running normalization state from.
"""
with open(path, 'rb') as f:
data = pickle.load(f)
self.set_state(data)
class StandardizationPreprocessor(Serializable):
"""
Preprocess observations from the environment using a running
standardization.
"""
def __init__(self, mdp_info, clip_obs=10., alpha=1e-32):
"""
Constructor.
Args:
mdp_info (MDPInfo): information of the MDP;
clip_obs (float, 10.): values to clip the normalized observations;
alpha (float, 1e-32): moving average catchup parameter for the
normalization.
"""
self._clip_obs = clip_obs
self._obs_shape = mdp_info.observation_space.shape
self._obs_runstand = RunningStandardization(shape=self._obs_shape,
alpha=alpha)
self._add_save_attr(_clip_obs='primitive',
_obs_shape='primitive',
_obs_runstand='mushroom')
def __call__(self, obs):
"""
Call function to normalize the observation.
Args:
obs (np.ndarray): observation to be normalized.
Returns:
Normalized observation array with the same shape.
"""
assert obs.shape == self._obs_shape, \
"Values given to running_norm have incorrect shape " \
"(obs shape: {}, expected shape: {})" \
.format(obs.shape, self._obs_shape)
self._obs_runstand.update_stats(obs)
norm_obs = np.clip(
(obs - self._obs_runstand.mean) / self._obs_runstand.std,
-self._clip_obs, self._clip_obs)
return norm_obs
def get_state(self):
"""
Returns:
A dictionary with the normalization state.
"""
return self._obs_runstand.get_state()
def set_state(self, data):
"""
Set the current normalization state from the data dict.
"""
self._obs_runstand.set_state(data)