A nest of tensor_spec.TensorSpec representing the
A nest of tensor_spec.BoundedTensorSpec representing the
(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
(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
Optional list of convolution layers parameters, where
each item is a length-three tuple indicating (filters, kernel_size,
Optional list of fully_connected parameters, where each
item is the number of units in the layer.
Optional list of dropout layer parameters, where
each item is the fraction of input units to drop. The dropout layers are
interleaved with the fully connected layers; there is a dropout layer
after each fully connected layer, except if the entry in the list is
None. This list must have the same length of fc_layer_params, or be
Activation function, e.g. tf.keras.activations.relu.
Initializer to use for the kernels of the conv and
dense layers. If none is provided a default variance_scaling_initializer
If True the outer_ranks of the observation are squashed into
the batch dimension. This allow encoding networks to be used with
observations with shape [BxTx...].
Float. The minimum allowed predicted variance. Predicted
variances less than this value will be clipped to this value.
Float. The maximum allowed predicted variance. Predicted
variances greater than this value will be clipped to this value.
The dtype to use by the convolution and fully connected layers.
A string representing the name of the network.
If input_tensor_spec contains more than one observation. Or
if action_spec contains more than one action.
Returns the spec of the input to the network of type InputSpec.
Get the list of all (nested) sub-layers used in this Network.
(Optional). Override or provide an input tensor spec
when creating variables.
Other arguments to network.call(), e.g. training=True.
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.
If no input_tensor_spec is provided, and the network did
not provide one during construction.