garage.tf.q_functions.discrete_cnn_q_function

Discrete CNN QFunction with CNN-MLP structure.

class DiscreteCNNQFunction(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(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(), dueling=False, layer_normalization=False)

Bases: garage.tf.models.Sequential

Inheritance diagram of garage.tf.q_functions.discrete_cnn_q_function.DiscreteCNNQFunction

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).

Parameters:
  • 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.
q_vals

Return the Q values, the output of the network.

Returns:Q values.
Return type:list[tf.Tensor]
input

Get input.

Returns:QFunction Input.
Return type:tf.Tensor
output

output of the model by default.

Type:tf.Tensor
inputs

inputs of the model by default.

Type:tf.Tensor
outputs

outputs of the model by default.

Type:tf.Tensor
parameters

Parameters of the model.

Returns:Parameters
Return type:np.ndarray
name

Name (str) of the model.

This is also the variable scope of the model.

Returns:Name of the model.
Return type:str
state_info_specs

State info specification.

Returns:
keys and shapes for the information related to the
module’s state when taking an action.
Return type:List[str]
state_info_keys

State info keys.

Returns:
keys for the information related to the module’s state
when taking an input.
Return type:List[str]
build(self, state_input, name)

Build the symbolic graph for q-network.

Parameters:
  • state_input (tf.Tensor) – The state input tf.Tensor to the network.
  • name (str) – Network variable scope.
Returns:

The tf.Tensor output of Discrete CNN QFunction.
Return type:

tf.Tensor
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:Clone of this object
Return type:garage.tf.q_functions.DiscreteCNNQFunction
network_input_spec(self)

Network input spec.

Returns:List of key(str) for the network inputs.
Return type:list[str]
network_output_spec(self)

Network output spec.

Returns:List of key(str) for the network outputs.
Return type:list[str]
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.
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]