class GaussianCNNBaseline(Baseline):
"""GaussianCNNBaseline With Model.
It fits the input data to a gaussian distribution estimated by a CNN.
Args:
env_spec (metarl.envs.env_spec.EnvSpec): Environment specification.
subsample_factor (float): The factor to subsample the data. By
default it is 1.0, which means using all the data.
regressor_args (dict): Arguments for regressor.
name (str): Name of baseline.
"""
def __init__(
self,
env_spec,
subsample_factor=1.,
regressor_args=None,
name='GaussianCNNBaseline',
):
if not isinstance(env_spec.observation_space, akro.Box) or \
not len(env_spec.observation_space.shape) in (2, 3):
raise ValueError(
'{} can only process 2D, 3D akro.Image or'
' akro.Box observations, but received an env_spec with '
'observation_space of type {} and shape {}'.format(
type(self).__name__,
type(env_spec.observation_space).__name__,
env_spec.observation_space.shape))
super().__init__(env_spec)
if regressor_args is None:
regressor_args = dict()
self._regressor = GaussianCNNRegressor(
input_shape=(env_spec.observation_space.shape),
output_dim=1,
subsample_factor=subsample_factor,
name=name,
**regressor_args)
self.name = name
self.env_spec = env_spec
def fit(self, paths):
"""Fit regressor based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
"""
observations = np.concatenate([p['observations'] for p in paths])
if isinstance(self.env_spec.observation_space, akro.Image):
observations = normalize_pixel_batch(observations)
returns = np.concatenate([p['returns'] for p in paths])
self._regressor.fit(observations, returns.reshape((-1, 1)))
def predict(self, path):
"""Predict value based on paths.
Args:
path (dict[numpy.ndarray]): Sample paths.
Returns:
numpy.ndarray: Predicted value.
"""
observations = path['observations']
if isinstance(self.env_spec.observation_space, akro.Image):
observations = normalize_pixel_batch(observations)
return self._regressor.predict(observations).flatten()
def get_param_values(self):
"""Get parameter values.
Returns:
List[np.ndarray]: A list of values of each parameter.
"""
return self._regressor.get_param_values()
def set_param_values(self, flattened_params):
"""Set param values.
Args:
flattened_params (np.ndarray): A numpy array of parameter values.
"""
self._regressor.set_param_values(flattened_params)
def get_params_internal(self):
"""Get the params, which are the trainable variables.
Returns:
List[tf.Variable]: A list of trainable variables in the current
variable scope.
"""
return self._regressor.get_params_internal()
class GaussianCNNBaseline(Baseline):
"""GaussianCNNBaseline With Model.
It fits the input data to a gaussian distribution estimated by a CNN.
Args:
env_spec (metarl.envs.env_spec.EnvSpec): Environment specification.
subsample_factor (float): The factor to subsample the data. By
default it is 1.0, which means using all the data.
regressor_args (dict): Arguments for regressor.
name (str): Name of baseline.
"""
def __init__(
self,
env_spec,
subsample_factor=1.,
regressor_args=None,
name='GaussianCNNBaseline',
):
super().__init__(env_spec)
if regressor_args is None:
regressor_args = dict()
self._regressor = GaussianCNNRegressor(
input_shape=(env_spec.observation_space.shape),
output_dim=1,
subsample_factor=subsample_factor,
name=name,
**regressor_args)
self.name = name
def fit(self, paths):
"""Fit regressor based on paths.
Args:
paths (dict[numpy.ndarray]): Sample paths.
"""
observations = np.concatenate([p['observations'] for p in paths])
returns = np.concatenate([p['returns'] for p in paths])
self._regressor.fit(observations, returns.reshape((-1, 1)))
def predict(self, path):
"""Predict value based on paths.
Args:
path (dict[numpy.ndarray]): Sample paths.
Returns:
numpy.ndarray: Predicted value.
"""
return self._regressor.predict(path['observations']).flatten()
def get_param_values(self):
"""Get parameter values.
Returns:
List[np.ndarray]: A list of values of each parameter.
"""
return self._regressor.get_param_values()
def set_param_values(self, flattened_params):
"""Set param values.
Args:
flattened_params (np.ndarray): A numpy array of parameter values.
"""
self._regressor.set_param_values(flattened_params)
def get_params_internal(self):
"""Get the params, which are the trainable variables.
Returns:
List[tf.Variable]: A list of trainable variables in the current
variable scope.
"""
return self._regressor.get_params_internal()