class_docstr: Feed Forward network.
class_docstr_tok: ["Feed","Forward","network","."]
docstr: Creates an instance of `QNetwork`.
  
  Args:
    input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
      input observations.
    action_spec: A nest of `tensor_spec.BoundedTensorSpec` representing the
      actions.
    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, 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
      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.
    q_layer_activation_fn: Activation function for the Q layer.
    name: A string representing the name of the network.
  
  Raises:
    ValueError: If `input_tensor_spec` contains more than one observation. Or
      if `action_spec` contains more than one action.
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code: def __init__(
      self,
      input_tensor_spec,
      action_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,
      q_layer_activation_fn=None,
      name="QNetwork",
  ):
      """Creates an instance of `QNetwork`.
  
      Args:
        input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
          input observations.
        action_spec: A nest of `tensor_spec.BoundedTensorSpec` representing the
          actions.
        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, 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
          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.
        q_layer_activation_fn: Activation function for the Q layer.
        name: A string representing the name of the network.
  
      Raises:
        ValueError: If `input_tensor_spec` contains more than one observation. Or
          if `action_spec` contains more than one action.
      """
      validate_specs(action_spec, input_tensor_spec)
      action_spec = tf.nest.flatten(action_spec)[0]
      num_actions = action_spec.maximum - action_spec.minimum + 1
      encoder_input_tensor_spec = input_tensor_spec
  
      encoder = encoding_network.EncodingNetwork(
          encoder_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,
      )
  
      q_value_layer = tf.keras.layers.Dense(
          num_actions,
          activation=q_layer_activation_fn,
          kernel_initializer=tf.random_uniform_initializer(
              minval=-0.03, maxval=0.03
          ),
          bias_initializer=tf.constant_initializer(-0.2),
          dtype=dtype,
      )
  
      super(QNetwork, self).__init__(
          input_tensor_spec=input_tensor_spec, state_spec=(), name=name
      )
  
      self._encoder = encoder
      self._q_value_layer = q_value_layer
  
file_path: agents/tf_agents/networks/q_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 QNetwork object from inherited base classes: network with example: obj = QNetwork(input_tensor_spec, action_spec, preprocessing_layers, preprocessing_combiner, conv_layer_params, fc_layer_params, dropout_layer_params, activation_fn, kernel_initializer, batch_squash, dtype, q_layer_activation_fn, name)