def __init__(self,
env_spec,
conv_filters,
conv_filter_sizes,
conv_strides,
conv_pad,
name='CategoricalCNNPolicy',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=tf.nn.softmax,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
layer_normalization=False):
if not isinstance(env_spec.action_space, akro.Discrete):
raise ValueError(
'CategoricalCNNPolicy only works with akro.Discrete action '
'space.')
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__(name, env_spec)
self.obs_dim = env_spec.observation_space.shape
self.action_dim = env_spec.action_space.n
self.model = Sequential(
CNNModel(filter_dims=conv_filter_sizes,
num_filters=conv_filters,
strides=conv_strides,
padding=conv_pad,
hidden_nonlinearity=hidden_nonlinearity,
name='CNNModel'),
MLPModel(output_dim=self.action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization,
name='MLPModel'))
self._initialize()
def __init__(self,
env_spec,
conv_filters,
conv_filter_sizes,
conv_strides,
conv_pad,
name='CategoricalConvPolicy',
hidden_sizes=[],
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=tf.nn.softmax,
output_w_init=tf.glorot_uniform_initializer(),
output_b_init=tf.zeros_initializer(),
layer_normalization=False):
assert isinstance(env_spec.action_space, akro.Discrete), (
'CategoricalConvPolicy only works with akro.Discrete action '
'space.')
super().__init__(name, env_spec)
self.obs_dim = env_spec.observation_space.shape
self.action_dim = env_spec.action_space.n
self.model = Sequential(
CNNModel(
filter_dims=conv_filter_sizes,
num_filters=conv_filters,
strides=conv_strides,
padding=conv_pad,
hidden_nonlinearity=hidden_nonlinearity,
name='CNNModel'),
MLPModel(
output_dim=self.action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization,
name='MLPModel'))
self._initialize()
class CategoricalConvPolicyWithModel(StochasticPolicy2):
"""
CategoricalConvPolicy with model.
A policy that contains a CNN and a MLP to make prediction based on
a categorical distribution.
It only works with akro.tf.Discrete action space.
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
conv_filter_sizes(tuple[int]): Dimension of the filters. For example,
(3, 5) means there are two convolutional layers. The filter for
first layer is of dimension (3 x 3) and the second one is of
dimension (5 x 5).
conv_filters(tuple[int]): Number of filters. For example, (3, 32) means
there are two convolutional layers. The filter for the first layer
has 3 channels and the second one with 32 channels.
conv_strides(tuple[int]): The stride of the sliding window. For
example, (1, 2) means there are two convolutional layers. The
stride of the filter for first layer is 1 and that of the second
layer is 2.
conv_pad (str): The type of padding algorithm to use,
either 'SAME' or 'VALID'.
name (str): Policy name, also the variable scope of the policy.
hidden_sizes (list[int]): Output dimension of dense layer(s).
For example, (32, 32) means the MLP of this policy consists
of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s). It should return a tf.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s). The function should return a
tf.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s). The function should return a
tf.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer. It should return a tf.Tensor. Set it to None to
maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s). The function should return a
tf.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s). The function should return a
tf.Tensor.
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
env_spec,
conv_filters,
conv_filter_sizes,
conv_strides,
conv_pad,
name='CategoricalConvPolicy',
hidden_sizes=[],
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=tf.nn.softmax,
output_w_init=tf.glorot_uniform_initializer(),
output_b_init=tf.zeros_initializer(),
layer_normalization=False):
assert isinstance(env_spec.action_space, Discrete), (
'CategoricalConvPolicy only works with akro.tf.Discrete'
'action space.')
super().__init__(name, env_spec)
self.obs_dim = env_spec.observation_space.shape
self.action_dim = env_spec.action_space.n
self.model = Sequential(
CNNModel(filter_dims=conv_filter_sizes,
num_filters=conv_filters,
strides=conv_strides,
padding=conv_pad,
hidden_nonlinearity=hidden_nonlinearity,
name='CNNModel'),
MLPModel(output_dim=self.action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization,
name='MLPModel'))
self._initialize()
def _initialize(self):
state_input = tf.placeholder(tf.float32, shape=(None, ) + self.obs_dim)
with tf.variable_scope(self._variable_scope):
self.model.build(state_input)
self._f_prob = tf.get_default_session().make_callable(
self.model.outputs, feed_list=[self.model.input])
@property
def vectorized(self):
"""Vectorized or not."""
return True
@overrides
def dist_info_sym(self, obs_var, state_info_vars=None, name=None):
"""Symbolic graph of the distribution."""
with tf.variable_scope(self._variable_scope):
prob = self.model.build(obs_var, name=name)
return dict(prob=prob)
@overrides
def dist_info(self, obs, state_infos=None):
"""Distribution info."""
prob = self._f_prob(obs)
return dict(prob=prob)
@overrides
def get_action(self, observation):
"""Return a single action."""
# flat_obs = self.observation_space.flatten(observation)
prob = self._f_prob([observation])[0]
action = self.action_space.weighted_sample(prob)
return action, dict(prob=prob)
def get_actions(self, observations):
"""Return multiple actions."""
# flat_obs = self.observation_space.flatten_n(observations)
probs = self._f_prob(observations)
actions = list(map(self.action_space.weighted_sample, probs))
return actions, dict(prob=probs)
@property
def distribution(self):
"""Policy distribution."""
return Categorical(self.action_dim)
def __getstate__(self):
"""Object.__getstate__."""
new_dict = self.__dict__.copy()
del new_dict['_f_prob']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__."""
self.__dict__.update(state)
self._initialize()
def __init__(self,
env_spec,
filters,
strides,
hidden_sizes=(256, ),
name=None,
padding='SAME',
max_pooling=False,
pool_strides=(2, 2),
pool_shapes=(2, 2),
cnn_hidden_nonlinearity=tf.nn.relu,
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=None,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
dueling=False,
layer_normalization=False):
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__(name)
self._env_spec = env_spec
self._action_dim = env_spec.action_space.n
self._filters = filters
self._strides = strides
self._hidden_sizes = hidden_sizes
self._padding = padding
self._max_pooling = max_pooling
self._pool_strides = pool_strides
self._pool_shapes = pool_shapes
self._cnn_hidden_nonlinearity = cnn_hidden_nonlinearity
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._layer_normalization = layer_normalization
self._dueling = dueling
self.obs_dim = self._env_spec.observation_space.shape
action_dim = self._env_spec.action_space.flat_dim
if not max_pooling:
cnn_model = CNNModel(filters=filters,
strides=strides,
padding=padding,
hidden_nonlinearity=cnn_hidden_nonlinearity)
else:
cnn_model = CNNModelWithMaxPooling(
filters=filters,
strides=strides,
padding=padding,
pool_strides=pool_strides,
pool_shapes=pool_shapes,
hidden_nonlinearity=cnn_hidden_nonlinearity)
if not dueling:
output_model = MLPModel(output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
else:
output_model = MLPDuelingModel(
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
self.model = Sequential(cnn_model, output_model)
self._initialize()
class DiscreteCNNQFunction(QFunction):
"""Q function based on a CNN-MLP structure for discrete action space.
This class implements a Q value network to predict Q based on the
input state and action. It uses an CNN and a MLP to fit the function
of Q(s, a).
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
filters (Tuple[Tuple[int, Tuple[int, int]], ...]): Number and dimension
of filters. For example, ((3, (3, 5)), (32, (3, 3))) means there
are two convolutional layers. The filter for the first layer have 3
channels and its shape is (3 x 5), while the filter for the second
layer have 32 channels and its shape is (3 x 3).
strides (tuple[int]): The stride of the sliding window. For example,
(1, 2) means there are two convolutional layers. The stride of the
filter for first layer is 1 and that of the second layer is 2.
hidden_sizes (list[int]): Output dimension of dense layer(s).
For example, (32, 32) means the MLP of this q-function consists of
two hidden layers, each with 32 hidden units.
name (str): Variable scope of the cnn.
padding (str): The type of padding algorithm to use,
either 'SAME' or 'VALID'.
max_pooling (bool): Boolean for using max pooling layer or not.
pool_shapes (tuple[int]): Dimension of the pooling layer(s). For
example, (2, 2) means that all the pooling layers have
shape (2, 2).
pool_strides (tuple[int]): The strides of the pooling layer(s). For
example, (2, 2) means that all the pooling layers have
strides (2, 2).
cnn_hidden_nonlinearity (callable): Activation function for
intermediate dense layer(s) in the CNN. It should return a
tf.Tensor. Set it to None to maintain a linear activation.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s) in the MLP. It should return a tf.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s) in the MLP. The function should
return a tf.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s) in the MLP. The function should
return a tf.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer in the MLP. It should return a tf.Tensor. Set it to None
to maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s) in the MLP. The function should return
a tf.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s) in the MLP. The function should return
a tf.Tensor.
dueling (bool): Bool for using dueling network or not.
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
env_spec,
filters,
strides,
hidden_sizes=(256, ),
name=None,
padding='SAME',
max_pooling=False,
pool_strides=(2, 2),
pool_shapes=(2, 2),
cnn_hidden_nonlinearity=tf.nn.relu,
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=None,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
dueling=False,
layer_normalization=False):
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__(name)
self._env_spec = env_spec
self._action_dim = env_spec.action_space.n
self._filters = filters
self._strides = strides
self._hidden_sizes = hidden_sizes
self._padding = padding
self._max_pooling = max_pooling
self._pool_strides = pool_strides
self._pool_shapes = pool_shapes
self._cnn_hidden_nonlinearity = cnn_hidden_nonlinearity
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._layer_normalization = layer_normalization
self._dueling = dueling
self.obs_dim = self._env_spec.observation_space.shape
action_dim = self._env_spec.action_space.flat_dim
if not max_pooling:
cnn_model = CNNModel(filters=filters,
strides=strides,
padding=padding,
hidden_nonlinearity=cnn_hidden_nonlinearity)
else:
cnn_model = CNNModelWithMaxPooling(
filters=filters,
strides=strides,
padding=padding,
pool_strides=pool_strides,
pool_shapes=pool_shapes,
hidden_nonlinearity=cnn_hidden_nonlinearity)
if not dueling:
output_model = MLPModel(output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
else:
output_model = MLPDuelingModel(
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
self.model = Sequential(cnn_model, output_model)
self._initialize()
def _initialize(self):
"""Initialize QFunction."""
if isinstance(self._env_spec.observation_space, akro.Image):
obs_ph = tf.compat.v1.placeholder(tf.uint8,
(None, ) + self.obs_dim,
name='obs')
augmented_obs_ph = tf.cast(obs_ph, tf.float32) / 255.0
else:
obs_ph = tf.compat.v1.placeholder(tf.float32,
(None, ) + self.obs_dim,
name='obs')
augmented_obs_ph = obs_ph
with tf.compat.v1.variable_scope(self.name) as vs:
self._variable_scope = vs
self.model.build(augmented_obs_ph)
self._obs_input = obs_ph
@property
def q_vals(self):
"""Return the Q values, the output of the network.
Return:
list[tf.Tensor]: Q values.
"""
return self.model.networks['default'].outputs
@property
def input(self):
"""Get input.
Return:
tf.Tensor: QFunction Input.
"""
return self._obs_input
# pylint: disable=arguments-differ
def get_qval_sym(self, state_input, name):
"""Symbolic graph for q-network.
Args:
state_input (tf.Tensor): The state input tf.Tensor to the network.
name (str): Network variable scope.
Return:
tf.Tensor: The tf.Tensor output of Discrete CNN QFunction.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
augmented_state_input = state_input
if isinstance(self._env_spec.observation_space, akro.Image):
augmented_state_input = tf.cast(state_input,
tf.float32) / 255.0
return self.model.build(augmented_state_input, name=name)
def clone(self, name):
"""Return a clone of the Q-function.
It only copies the configuration of the Q-function,
not the parameters.
Args:
name(str) : Name of the newly created q-function.
Returns:
garage.tf.q_functions.DiscreteCNNQFunction: Clone of this object
"""
return self.__class__(name=name,
env_spec=self._env_spec,
filters=self._filters,
strides=self._strides,
hidden_sizes=self._hidden_sizes,
padding=self._padding,
max_pooling=self._max_pooling,
pool_shapes=self._pool_shapes,
pool_strides=self._pool_strides,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
dueling=self._dueling,
layer_normalization=self._layer_normalization)
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.
"""
new_dict = self.__dict__.copy()
del new_dict['_obs_input']
return new_dict
def __init__(self,
env_spec,
filter_dims,
num_filters,
strides,
hidden_sizes=[256],
name=None,
padding='SAME',
max_pooling=False,
pool_strides=(2, 2),
pool_shapes=(2, 2),
cnn_hidden_nonlinearity=tf.nn.relu,
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=None,
output_w_init=tf.glorot_uniform_initializer(),
output_b_init=tf.zeros_initializer(),
dueling=False,
layer_normalization=False):
super().__init__(name)
self._env_spec = env_spec
self._action_dim = env_spec.action_space.n
self._filter_dims = filter_dims
self._num_filters = num_filters
self._strides = strides
self._hidden_sizes = hidden_sizes
self._padding = padding
self._max_pooling = max_pooling
self._pool_strides = pool_strides
self._pool_shapes = pool_shapes
self._cnn_hidden_nonlinearity = cnn_hidden_nonlinearity
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._layer_normalization = layer_normalization
self._dueling = dueling
self.obs_dim = self._env_spec.observation_space.shape
action_dim = self._env_spec.action_space.flat_dim
if not max_pooling:
cnn_model = CNNModel(filter_dims=filter_dims,
num_filters=num_filters,
strides=strides,
padding=padding,
hidden_nonlinearity=cnn_hidden_nonlinearity)
else:
cnn_model = CNNModelWithMaxPooling(
filter_dims=filter_dims,
num_filters=num_filters,
strides=strides,
padding=padding,
pool_strides=pool_strides,
pool_shapes=pool_shapes,
hidden_nonlinearity=cnn_hidden_nonlinearity)
if not dueling:
output_model = MLPModel(output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
else:
output_model = MLPDuelingModel(
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
self.model = Sequential(cnn_model, output_model)
self._initialize()
class DiscreteCNNQFunction(QFunction):
"""Q function based on a CNN-MLP structure for discrete action space.
This class implements a Q value network to predict Q based on the
input state and action. It uses an CNN and a MLP to fit the function
of Q(s, a).
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
filter_dims (tuple[int]): Dimension of the filters. For example,
(3, 5) means there are two convolutional layers. The filter for
first layer is of dimension (3 x 3) and the second one is of
dimension (5 x 5).
num_filters (tuple[int]): Number of filters. For example, (3, 32) means
there are two convolutional layers. The filter for the first layer
has 3 channels and the second one with 32 channels.
strides (tuple[int]): The stride of the sliding window. For example,
(1, 2) means there are two convolutional layers. The stride of the
filter for first layer is 1 and that of the second layer is 2.
hidden_sizes (list[int]): Output dimension of dense layer(s).
For example, (32, 32) means the MLP of this q-function consists of
two hidden layers, each with 32 hidden units.
name (str): Variable scope of the cnn.
padding (str): The type of padding algorithm to use,
either 'SAME' or 'VALID'.
max_pooling (bool): Boolean for using max pooling layer or not.
pool_shapes (tuple[int]): Dimension of the pooling layer(s). For
example, (2, 2) means that all the pooling layers have
shape (2, 2).
pool_strides (tuple[int]): The strides of the pooling layer(s). For
example, (2, 2) means that all the pooling layers have
strides (2, 2).
cnn_hidden_nonlinearity (callable): Activation function for
intermediate dense layer(s) in the CNN. It should return a
tf.Tensor. Set it to None to maintain a linear activation.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s) in the MLP. It should return a tf.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s) in the MLP. The function should
return a tf.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s) in the MLP. The function should
return a tf.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer in the MLP. It should return a tf.Tensor. Set it to None
to maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s) in the MLP. The function should return
a tf.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s) in the MLP. The function should return
a tf.Tensor.
dueling (bool): Bool for using dueling network or not.
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
env_spec,
filter_dims,
num_filters,
strides,
hidden_sizes=[256],
name=None,
padding='SAME',
max_pooling=False,
pool_strides=(2, 2),
pool_shapes=(2, 2),
cnn_hidden_nonlinearity=tf.nn.relu,
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.glorot_uniform_initializer(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=None,
output_w_init=tf.glorot_uniform_initializer(),
output_b_init=tf.zeros_initializer(),
dueling=False,
layer_normalization=False):
super().__init__(name)
self._env_spec = env_spec
self._action_dim = env_spec.action_space.n
self._filter_dims = filter_dims
self._num_filters = num_filters
self._strides = strides
self._hidden_sizes = hidden_sizes
self._padding = padding
self._max_pooling = max_pooling
self._pool_strides = pool_strides
self._pool_shapes = pool_shapes
self._cnn_hidden_nonlinearity = cnn_hidden_nonlinearity
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._layer_normalization = layer_normalization
self._dueling = dueling
self.obs_dim = self._env_spec.observation_space.shape
action_dim = self._env_spec.action_space.flat_dim
if not max_pooling:
cnn_model = CNNModel(filter_dims=filter_dims,
num_filters=num_filters,
strides=strides,
padding=padding,
hidden_nonlinearity=cnn_hidden_nonlinearity)
else:
cnn_model = CNNModelWithMaxPooling(
filter_dims=filter_dims,
num_filters=num_filters,
strides=strides,
padding=padding,
pool_strides=pool_strides,
pool_shapes=pool_shapes,
hidden_nonlinearity=cnn_hidden_nonlinearity)
if not dueling:
output_model = MLPModel(output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
else:
output_model = MLPDuelingModel(
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization)
self.model = Sequential(cnn_model, output_model)
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) as vs:
self._variable_scope = vs
self.model.build(obs_ph)
@property
def q_vals(self):
"""Q values."""
return self.model.networks['default'].outputs
@property
def input(self):
"""Input tf.Tensor of the Q-function."""
return self.model.networks['default'].input
def get_qval_sym(self, state_input, name):
"""Symbolic graph for q-network.
Args:
state_input (tf.Tensor): The state input tf.Tensor to the network.
name (str): Network variable scope.
Return:
The tf.Tensor output of Discrete CNN QFunction.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
return self.model.build(state_input, name=name)
def clone(self, name):
"""Return a clone of the Q-function.
It only copies the configuration of the Q-function,
not the parameters.
Args:
name: Name of the newly created q-function.
"""
return self.__class__(name=name,
env_spec=self._env_spec,
filter_dims=self._filter_dims,
num_filters=self._num_filters,
strides=self._strides,
hidden_sizes=self._hidden_sizes,
padding=self._padding,
max_pooling=self._max_pooling,
pool_shapes=self._pool_shapes,
pool_strides=self._pool_strides,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
dueling=self._dueling,
layer_normalization=self._layer_normalization)
def __setstate__(self, state):
"""Object.__setstate__."""
self.__dict__.update(state)
self._initialize()
class CategoricalCNNPolicy(StochasticPolicy):
"""CategoricalCNNPolicy.
A policy that contains a CNN and a MLP to make prediction based on
a categorical distribution.
It only works with akro.Discrete action space.
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
conv_filter_sizes(tuple[int]): Dimension of the filters. For example,
(3, 5) means there are two convolutional layers. The filter for
first layer is of dimension (3 x 3) and the second one is of
dimension (5 x 5).
conv_filters(tuple[int]): Number of filters. For example, (3, 32) means
there are two convolutional layers. The filter for the first layer
has 3 channels and the second one with 32 channels.
conv_strides(tuple[int]): The stride of the sliding window. For
example, (1, 2) means there are two convolutional layers. The
stride of the filter for first layer is 1 and that of the second
layer is 2.
conv_pad (str): The type of padding algorithm to use,
either 'SAME' or 'VALID'.
name (str): Policy name, also the variable scope of the policy.
hidden_sizes (list[int]): Output dimension of dense layer(s).
For example, (32, 32) means the MLP of this policy consists
of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s). It should return a tf.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s). The function should return a
tf.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s). The function should return a
tf.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer. It should return a tf.Tensor. Set it to None to
maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s). The function should return a
tf.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s). The function should return a
tf.Tensor.
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
env_spec,
conv_filters,
conv_filter_sizes,
conv_strides,
conv_pad,
name='CategoricalCNNPolicy',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.relu,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=tf.nn.softmax,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
layer_normalization=False):
if not isinstance(env_spec.action_space, akro.Discrete):
raise ValueError(
'CategoricalCNNPolicy only works with akro.Discrete action '
'space.')
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__(name, env_spec)
self.obs_dim = env_spec.observation_space.shape
self.action_dim = env_spec.action_space.n
self.model = Sequential(
CNNModel(filter_dims=conv_filter_sizes,
num_filters=conv_filters,
strides=conv_strides,
padding=conv_pad,
hidden_nonlinearity=hidden_nonlinearity,
name='CNNModel'),
MLPModel(output_dim=self.action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
layer_normalization=layer_normalization,
name='MLPModel'))
self._initialize()
def _initialize(self):
if isinstance(self.env_spec.observation_space, akro.Image):
state_input = tf.compat.v1.placeholder(tf.uint8,
shape=(None, ) +
self.obs_dim)
state_input = tf.cast(state_input, tf.float32)
state_input /= 255.0
else:
state_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
self.obs_dim)
with tf.compat.v1.variable_scope(self.name) as vs:
self._variable_scope = vs
self.model.build(state_input)
self._f_prob = tf.compat.v1.get_default_session().make_callable(
self.model.outputs, feed_list=[self.model.input])
@property
def vectorized(self):
"""Vectorized or not.
Returns:
bool: True if primitive supports vectorized operations.
"""
return True
def dist_info_sym(self, obs_var, state_info_vars=None, name=None):
"""Build a symbolic graph of the distribution parameters.
Args:
obs_var (tf.Tensor): Tensor input for symbolic graph.
state_info_vars (dict[np.ndarray]): Extra state information, e.g.
previous action.
name (str): Name for symbolic graph.
Returns:
dict[tf.Tensor]: Outputs of the symbolic graph of distribution
parameters.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
if isinstance(self.env_spec.observation_space, akro.Image):
obs_var = tf.cast(obs_var, tf.float32) / 255.0
prob = self.model.build(obs_var, name=name)
return dict(prob=prob)
def dist_info(self, obs, state_infos=None):
"""Get distribution parameters.
Args:
obs (np.ndarray): Observation input.
state_infos (dict[np.ndarray]): Extra state information, e.g.
previous action.
Returns:
dict[np.ndarray]: Distribution parameters.
"""
prob = self._f_prob(obs)
return dict(prob=prob)
def get_action(self, observation):
"""Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Predicted action.
dict[str: np.ndarray]: Action distribution.
"""
if len(observation.shape) < len(self.obs_dim):
observation = self.env_spec.observation_space.unflatten(
observation)
prob = self._f_prob([observation])[0]
action = self.action_space.weighted_sample(prob)
return action, dict(prob=prob)
def get_actions(self, observations):
"""Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Predicted actions.
dict[str: np.ndarray]: Action distributions.
"""
if len(observations[0].shape) < len(self.obs_dim):
observations = self.env_spec.observation_space.unflatten_n(
observations)
probs = self._f_prob(observations)
actions = list(map(self.action_space.weighted_sample, probs))
return actions, dict(prob=probs)
@property
def distribution(self):
"""Policy distribution.
Returns:
garage.tf.distributions.Categorical: Policy distribution.
"""
return Categorical(self.action_dim)
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: The state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_prob']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): Unpickled state.
"""
super().__setstate__(state)
self._initialize()
