class_docstr: Feed Forward value network. Reduces to 1 value output per batch item.
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docstr: Creates an instance of `ValueNetwork`.
  
  Network supports calls with shape outer_rank + observation_spec.shape. Note
  outer_rank must be at least 1.
  
  Args:
    input_tensor_spec: A `tensor_spec.TensorSpec` or a tuple of specs
      representing the input observations.
    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 layers parameters, where
      each item is a length-three tuple indicating (filters, kernel_size,
      stride).
    fc_layer_params: Optional list of fully_connected parameters, where each
      item is the number of units in the layer.
    dropout_layer_params: Optional list of dropout layer parameters, each item
      is the fraction of input units to drop or a dictionary of parameters
      according to the keras.Dropout documentation. The additional parameter
      `permanent`, if set to True, allows to apply dropout at inference for
      approximated Bayesian inference. 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 None.
    activation_fn: Activation function, e.g. tf.keras.activations.relu,.
    kernel_initializer: Initializer to use for the kernels of the conv and
      dense layers. If none is provided a default variance_scaling_initializer
    batch_squash: 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...].
    dtype: The dtype to use by the convolution and fully connected layers.
    name: A string representing name of the network.
  
  Raises:
    ValueError: If input_tensor_spec is not an instance of network.InputSpec.
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code: def __init__(
      self,
      input_tensor_spec,
      preprocessing_layers=None,
      preprocessing_combiner=None,
      conv_layer_params=None,
      fc_layer_params=(75, 40),
      dropout_layer_params=None,
      activation_fn=tf.keras.activations.relu,
      kernel_initializer=None,
      batch_squash=True,
      dtype=tf.float32,
      name="ValueNetwork",
  ):
      """Creates an instance of `ValueNetwork`.
  
      Network supports calls with shape outer_rank + observation_spec.shape. Note
      outer_rank must be at least 1.
  
      Args:
        input_tensor_spec: A `tensor_spec.TensorSpec` or a tuple of specs
          representing the input observations.
        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 layers parameters, where
          each item is a length-three tuple indicating (filters, kernel_size,
          stride).
        fc_layer_params: Optional list of fully_connected parameters, where each
          item is the number of units in the layer.
        dropout_layer_params: Optional list of dropout layer parameters, each item
          is the fraction of input units to drop or a dictionary of parameters
          according to the keras.Dropout documentation. The additional parameter
          `permanent`, if set to True, allows to apply dropout at inference for
          approximated Bayesian inference. 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 None.
        activation_fn: Activation function, e.g. tf.keras.activations.relu,.
        kernel_initializer: Initializer to use for the kernels of the conv and
          dense layers. If none is provided a default variance_scaling_initializer
        batch_squash: 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...].
        dtype: The dtype to use by the convolution and fully connected layers.
        name: A string representing name of the network.
  
      Raises:
        ValueError: If input_tensor_spec is not an instance of network.InputSpec.
      """
      super(ValueNetwork, self).__init__(
          input_tensor_spec=input_tensor_spec, state_spec=(), name=name
      )
  
      if not kernel_initializer:
          kernel_initializer = (
              tf.compat.v1.keras.initializers.glorot_uniform()
          )
  
      self._encoder = encoding_network.EncodingNetwork(
          input_tensor_spec,
          preprocessing_layers=preprocessing_layers,
          preprocessing_combiner=preprocessing_combiner,
          conv_layer_params=conv_layer_params,
          fc_layer_params=fc_layer_params,
          dropout_layer_params=dropout_layer_params,
          activation_fn=activation_fn,
          kernel_initializer=kernel_initializer,
          batch_squash=batch_squash,
          dtype=dtype,
      )
  
      self._postprocessing_layers = tf.keras.layers.Dense(
          1,
          activation=None,
          kernel_initializer=tf.random_uniform_initializer(
              minval=-0.03, maxval=0.03
          ),
      )
  
file_path: agents/tf_agents/networks/value_network.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gin
  import tensorflow
  from tf_agents.networks import encoding_network
  from tf_agents.networks import network
label_desc: Use image node_id 1 to create a new ValueNetwork object from inherited base classes: network with example: obj = ValueNetwork(input_tensor_spec, preprocessing_layers, preprocessing_combiner, conv_layer_params, fc_layer_params, dropout_layer_params, activation_fn, kernel_initializer, batch_squash, dtype, name)