garage.tf.q_functions.continuous_mlp_q_function module

Continuous MLP QFunction.

class ContinuousMLPQFunction(env_spec, name='ContinuousMLPQFunction', hidden_sizes=(32, 32), action_merge_layer=-2, hidden_nonlinearity=<function relu>, hidden_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, hidden_b_init=<tensorflow.python.ops.init_ops.Zeros object>, output_nonlinearity=None, output_w_init=<tensorflow.python.ops.init_ops.GlorotUniform object>, output_b_init=<tensorflow.python.ops.init_ops.Zeros object>, input_include_goal=False, layer_normalization=False)[source]

Bases: garage.tf.q_functions.base.QFunction

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 (garage.envs.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.
  • input_include_goal (bool) – Whether input includes goal.
  • layer_normalization (bool) – Bool for using layer normalization.
clone(name)[source]

Return a clone of the Q-function.

It only copies the configuration of the Q-function, not the parameters.

Parameters:name (str) – Name of the newly created q-function.
Returns:A new instance with same arguments.
Return type:ContinuousMLPQFunction
get_qval(observation, action)[source]

Q Value of the network.

Parameters:
  • observation (np.ndarray) – Observation input.
  • action (np.ndarray) – Action input.
Returns:

Q values.
Return type:

np.ndarray
get_qval_sym(state_input, action_input, name)[source]

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 Discrete MLP QFunction.
Return type:

tf.Tensor
inputs

Return the input tensor.

Returns:The input tensors of the model.
Return type:tf.Tensor