tf_agents.networks.categorical_q_network.CategoricalQNetwork

Creates a categorical Q-network.

Inherits From: Network

tf_agents.networks.categorical_q_network.CategoricalQNetwork(
    input_tensor_spec,
    action_spec,
    num_atoms=51,
    preprocessing_layers=None,
    preprocessing_combiner=None,
    conv_layer_params=None,
    fc_layer_params=None,
    activation_fn=tf.nn.relu,
    name='CategoricalQNetwork'
)

Used in the notebooks

Used in the tutorials

It can be used to take an input of batched observations and outputs ([batch_size, num_actions, num_atoms], network's state).

The first element of the output is a batch of logits based on the distribution called C51 from Bellemare et al., 2017 (https://arxiv.org/abs/1707.06887). The logits are used to compute approximate probability distributions for Q-values for each potential action, by computing the probabilities at the 51 points (called atoms) in np.linspace(-10.0, 10.0, 51).

input_tensor_spec A tensor_spec.TensorSpec specifying the observation spec.
action_spec A tensor_spec.BoundedTensorSpec representing the actions.
num_atoms The number of atoms to use in our approximate probability distributions. Defaults to 51 to produce C51.
preprocessing_layers (Optional.) A nest of tf.keras.layers.Layer representing preprocessing for the different observations. All of these layers must not be already built. For more details see the documentation of networks.EncodingNetwork.
preprocessing_combiner (Optional.) A keras layer that takes a flat list of tensors and combines them. Good options include tf.keras.layers.Add and tf.keras.layers.Concatenate(axis=-1). This layer must not be already built. For more details see the documentation of networks.EncodingNetwork.
conv_layer_params Optional list of convolution layer parameters for observations, where each item is a length-three tuple indicating (num_units, kernel_size, stride).
fc_layer_params Optional list of fully connected parameters for observations, where each item is the number of units in the layer.
activation_fn Activation function, e.g. tf.nn.relu or tf.nn.leaky_relu.
name A string representing the name of the network.

TypeError action_spec is not a BoundedTensorSpec.

input_tensor_spec Returns the spec of the input to the network of type InputSpec.
layers Get the list of all (nested) sub-layers used in this Network.
num_atoms

state_spec

Methods

copy

View source

copy(
    **kwargs
)

Create a shallow copy of this network.

Args
**kwargs Args to override when recreating this network. Commonly overridden args include 'name'.

Returns
A shallow copy of this network.

create_variables

View source

create_variables(
    input_tensor_spec=None, **kwargs
)

Force creation of the network's variables.

Return output specs.

Args
input_tensor_spec (Optional). Override or provide an input tensor spec when creating variables.
**kwargs Other arguments to network.call(), e.g. training=True.

Returns
Output specs - a nested spec calculated from the outputs (excluding any batch dimensions). If any of the output elements is a tfp Distribution, the associated spec entry returned is a DistributionSpec.

Raises
ValueError If no input_tensor_spec is provided, and the network did not provide one during construction.

get_initial_state

View source

get_initial_state(
    batch_size=None
)

Returns an initial state usable by the network.

Args
batch_size Tensor or constant: size of the batch dimension. Can be None in which case not dimensions gets added.

Returns
A nested object of type self.state_spec containing properly initialized Tensors.

get_layer

View source

get_layer(
    name=None, index=None
)

Retrieves a layer based on either its name (unique) or index.

If name and index are both provided, index will take precedence. Indices are based on order of horizontal graph traversal (bottom-up).

Args
name String, name of layer.
index Integer, index of layer.

Returns
A layer instance.

Raises
ValueError In case of invalid layer name or index.

summary

View source

summary(
    line_length=None, positions=None, print_fn=None
)

Prints a string summary of the network.

Args
line_length Total length of printed lines (e.g. set this to adapt the display to different terminal window sizes).
positions Relative or absolute positions of log elements in each line. If not provided, defaults to [.33, .55, .67, 1.].
print_fn Print function to use. Defaults to print. It will be called on each line of the summary. You can set it to a custom function in order to capture the string summary.

Raises
ValueError if summary() is called before the model is built.