garage.tf.q_functions.continuous_cnn_q_function
¶
Continuous CNN QFunction with CNN-MLP structure.
-
class
ContinuousCNNQFunction
(env_spec, filters, strides, hidden_sizes=(256), action_merge_layer=- 2, 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(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), layer_normalization=False)¶ Bases:
garage.tf.models.CNNMLPMergeModel
Q function based on a CNN-MLP structure for continuous 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).
- Parameters
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 (tuple[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.
action_merge_layer (int) – The index of layers at which to concatenate action inputs with the network. The indexing works like standard python list indexing. Index of 0 refers to the input layer (observation input) while an index of -1 points to the last hidden layer. Default parameter points to second layer from the end.
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.
layer_normalization (bool) – Bool for using layer normalization or not.
-
property
inputs
(self)¶ tuple[tf.Tensor]: The observation and action input tensors.
The returned tuple contains two tensors. The first is the observation tensor with shape \((N, O*)\), and the second is the action tensor with shape \((N, A*)\).
-
get_qval
(self, observation, action)¶ Q Value of the network.
- Parameters
observation (np.ndarray) – Observation input of shape \((N, O*)\).
action (np.ndarray) – Action input of shape \((N, A*)\).
- Returns
- Array of shape \((N, )\) containing Q values
corresponding to each (obs, act) pair.
- Return type
np.ndarray
-
build
(self, state_input, action_input, name)¶ Build the symbolic graph for q-network.
- Parameters
state_input (tf.Tensor) – The state input tf.Tensor of shape \((N, O*)\).
action_input (tf.Tensor) – The action input tf.Tensor of shape \((N, A*)\).
name (str) – Network variable scope.
- Returns
The output Q value tensor of shape \((N, )\).
- Return type
tf.Tensor
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clone
(self, name)¶ Return a clone of the Q-function.
It copies the configuration of the primitive and also the parameters.
- Parameters
name (str) – Name of the newly created q-function.
- Returns
Cloned Q function.
- Return type
-
network_input_spec
(self)¶ Network input spec.
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network_output_spec
(self)¶ Network output spec.
-
property
parameters
(self)¶ Parameters of the model.
- Returns
Parameters
- Return type
np.ndarray
-
property
name
(self)¶ Name (str) of the model.
This is also the variable scope of the model.
- Returns
Name of the model.
- Return type
-
property
input
(self)¶ Default input of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the input of the network.
- Returns
Default input of the model.
- Return type
tf.Tensor
-
property
output
(self)¶ Default output of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the output of the network.
- Returns
Default output of the model.
- Return type
tf.Tensor
-
property
outputs
(self)¶ Default outputs of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the outputs of the network.
- Returns
Default outputs of the model.
- Return type
list[tf.Tensor]
-
reset
(self, do_resets=None)¶ Reset the module.
This is effective only to recurrent modules. do_resets is effective only to vectoried modules.
For a vectorized modules, do_resets is an array of boolean indicating which internal states to be reset. The length of do_resets should be equal to the length of inputs.
- Parameters
do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
-
property
state_info_specs
(self)¶ State info specification.
- Returns
- keys and shapes for the information related to the
module’s state when taking an action.
- Return type
List[str]
-
property
state_info_keys
(self)¶ State info keys.
- Returns
- keys for the information related to the module’s state
when taking an input.
- Return type
List[str]
-
terminate
(self)¶ Clean up operation.
-
get_trainable_vars
(self)¶ Get trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
-
get_global_vars
(self)¶ Get global variables.
- Returns
- A list of global variables in the current
variable scope.
- Return type
List[tf.Variable]
-
get_regularizable_vars
(self)¶ Get all network weight variables in the current scope.
- Returns
- A list of network weight variables in the
current variable scope.
- Return type
List[tf.Variable]
-
get_params
(self)¶ Get the trainable variables.
- Returns
- A list of trainable variables in the current
variable scope.
- Return type
List[tf.Variable]
-
get_param_shapes
(self)¶ Get parameter shapes.
- Returns
A list of variable shapes.
- Return type
List[tuple]
-
get_param_values
(self)¶ Get param values.
- Returns
- Values of the parameters evaluated in
the current session
- Return type
np.ndarray
-
set_param_values
(self, param_values)¶ Set param values.
- Parameters
param_values (np.ndarray) – A numpy array of parameter values.
-
flat_to_params
(self, flattened_params)¶ Unflatten tensors according to their respective shapes.
- Parameters
flattened_params (np.ndarray) – A numpy array of flattened params.
- Returns
- A list of parameters reshaped to the
shapes specified.
- Return type
List[np.ndarray]