class SimpleQFunction(QFunction2):
"""Simple QFunction for testing."""
def __init__(self, env_spec, name='SimpleQFunction'):
super().__init__(name)
self.obs_dim = env_spec.observation_space.shape
action_dim = env_spec.observation_space.flat_dim
self.model = SimpleMLPModel(output_dim=action_dim)
self._initialize()
def _initialize(self):
obs_ph = tf.compat.v1.placeholder(tf.float32, (None, ) + self.obs_dim,
name='obs')
with tf.compat.v1.variable_scope(self.name, reuse=False) as vs:
self._variable_scope = vs
self.model.build(obs_ph)
@property
def q_vals(self):
return self.model.networks['default'].outputs
def __setstate__(self, state):
self.__dict__.update(state)
self._initialize()
class SimpleQFunction(QFunction):
"""Simple QFunction for testing.
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
name (str): Name of the q-function, also serves as the variable scope.
"""
def __init__(self, env_spec, name='SimpleQFunction'):
super().__init__(name)
self.obs_dim = env_spec.observation_space.shape
action_dim = env_spec.observation_space.flat_dim
self.model = SimpleMLPModel(output_dim=action_dim)
self._q_val = None
self._initialize()
def _initialize(self):
"""Initialize QFunction."""
obs_ph = tf.compat.v1.placeholder(tf.float32, (None, ) + self.obs_dim,
name='obs')
with tf.compat.v1.variable_scope(self.name, reuse=False) as vs:
self._variable_scope = vs
self._q_val = self.model.build(obs_ph).outputs
@property
def q_vals(self):
"""Return the Q values, the output of the network.
Return:
list[tf.Tensor]: Q values.
"""
return self._q_val
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): Unpickled state.
"""
self.__dict__.update(state)
self._initialize()
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = self.__dict__.copy()
del new_dict['_q_val']
return new_dict
class SimpleMLPRegressor(Regressor2):
"""Simple GaussianMLPRegressor for testing."""
def __init__(self, input_shape, output_dim, name, *args, **kwargs):
super().__init__(input_shape, output_dim, name)
self.model = SimpleMLPModel(output_dim=self._output_dim,
name='SimpleMLPModel')
self._initialize()
def _initialize(self):
input_ph = tf.placeholder(tf.float32,
shape=(None, ) + self._input_shape)
with tf.variable_scope(self._name) as vs:
self._variable_scope = vs
self.model.build(input_ph)
self.ys = None
def fit(self, xs, ys):
self.ys = ys
def predict(self, xs):
if self.ys is None:
outputs = tf.get_default_session().run(
self.model.networks['default'].outputs,
feed_dict={self.model.networks['default'].input: xs})
self.ys = outputs
return self.ys
def get_params_internal(self, *args, **kwargs):
return self._variable_scope.trainable_variables()
def __setstate__(self, state):
"""Object.__setstate__."""
super().__setstate__(state)
self._initialize()
class SimpleMLPRegressor(Regressor):
"""Simple MLPRegressor for testing.
Args:
input_shape (tuple[int]): Input shape of the training data.
output_dim (int): Output dimension of the model.
name (str): Model name, also the variable scope.
args (list): Unused positionl arguments.
kwargs (dict): Unused keyword arguments.
"""
def __init__(self, input_shape, output_dim, name, *args, **kwargs):
super().__init__(input_shape, output_dim, name)
del args, kwargs
self.model = SimpleMLPModel(output_dim=self._output_dim,
name='SimpleMLPModel')
self._ys = None
self._initialize()
def _initialize(self):
"""Initialize graph."""
input_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + self._input_shape)
with tf.compat.v1.variable_scope(self._name) as vs:
self._variable_scope = vs
self.model.build(input_ph)
@property
def recurrent(self):
"""bool: If this module has a hidden state."""
return False
@property
def vectorized(self):
"""bool: If this module supports vectorization input."""
return True
def fit(self, xs, ys):
"""Fit with input data xs and label ys.
Args:
xs (numpy.ndarray): Input data.
ys (numpy.ndarray): Label of input data.
"""
self._ys = ys
def predict(self, xs):
"""Predict ys based on input xs.
Args:
xs (numpy.ndarray): Input data.
Return:
np.ndarray: The predicted ys.
"""
if self._ys is None:
outputs = tf.compat.v1.get_default_session().run(
self.model.networks['default'].outputs,
feed_dict={self.model.networks['default'].input: xs})
self._ys = outputs
return self._ys
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._variable_scope.trainable_variables()
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): Unpickled state.
"""
super().__setstate__(state)
self._initialize()
