garage.tf.q_functions.continuous_mlp_q_function

Continuous MLP QFunction.

class ContinuousMLPQFunction(env_spec, name='ContinuousMLPQFunction', hidden_sizes=(32, 32), action_merge_layer=- 2, 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.MLPMergeModel

Inheritance diagram of garage.tf.q_functions.continuous_mlp_q_function.ContinuousMLPQFunction

Continuous MLP QFunction.

This class implements a q value network to predict q based on the input state and action. It uses an MLP to fit the function of Q(s, a).

Parameters
  • env_spec (EnvSpec) – Environment specification.

  • name (str) – Name of the q-function, also serves as the variable scope.

  • 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.

  • 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.

  • 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.

get_qval(self, observation, action)

Q Value of the network.

Parameters
  • observation (np.ndarray) – Observation input.

  • action (np.ndarray) – Action input.

Returns

Q values.

Return type

np.ndarray

property inputs(self)

Return the input tensor.

Returns

The input tensors of the model.

Return type

tf.Tensor

build(self, state_input, action_input, name)

Build the symbolic graph for q-network.

Parameters
  • state_input (tf.Tensor) – The state input tf.Tensor to the network.

  • action_input (tf.Tensor) – The action input tf.Tensor to the network.

  • name (str) – Network variable scope.

Returns

The output of Continuous MLP 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

A new instance with same arguments.

Return type

ContinuousMLPQFunction

network_input_spec(self)

Network input spec.

Returns

List of key(str) for the network outputs.

Return type

list[str]

network_output_spec(self)

Network output spec.

Returns

List of key(str) for the network outputs.

Return type

list[str]

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

str

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]