class_docstr: Recurrent network.
class_docstr_tok: ["Recurrent","network","."]
docstr: Creates an instance of `QRnnNetwork`.
  
  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).
    input_fc_layer_params: Optional list of fully connected parameters, where
      each item is the number of units in the layer. These feed into the
      recurrent layer.
    lstm_size: An iterable of ints specifying the LSTM cell sizes to use.
    output_fc_layer_params: Optional list of fully connected parameters, where
      each item is the number of units in the layer. These are applied on top
      of the recurrent layer.
    activation_fn: Activation function, e.g. tf.keras.activations.relu,.
    rnn_construction_fn: (Optional.) Alternate RNN construction function, e.g.
      tf.keras.layers.LSTM, tf.keras.layers.CuDNNLSTM. It is invalid to
      provide both rnn_construction_fn and lstm_size.
    rnn_construction_kwargs: (Optional.) Dictionary or arguments to pass to
      rnn_construction_fn.  The RNN will be constructed via:  ``` rnn_layer =
      rnn_construction_fn(**rnn_construction_kwargs) ```
    dtype: The dtype to use by the convolution, LSTM, and fully connected
      layers.
    name: A string representing name of the network.
  
  Raises:
    ValueError: If any of `preprocessing_layers` is already built.
    ValueError: If `preprocessing_combiner` is already built.
    ValueError: If `action_spec` contains more than one action.
    ValueError: If neither `lstm_size` nor `rnn_construction_fn` are provided.
    ValueError: If both `lstm_size` and `rnn_construction_fn` are provided.
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code: def __init__(
      self,
      input_tensor_spec,
      action_spec,
      preprocessing_layers=None,
      preprocessing_combiner=None,
      conv_layer_params=None,
      input_fc_layer_params=(75, 40),
      lstm_size=None,
      output_fc_layer_params=(75, 40),
      activation_fn=tf.keras.activations.relu,
      rnn_construction_fn=None,
      rnn_construction_kwargs=None,
      dtype=tf.float32,
      name="QRnnNetwork",
  ):
      """Creates an instance of `QRnnNetwork`.
  
      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).
        input_fc_layer_params: Optional list of fully connected parameters, where
          each item is the number of units in the layer. These feed into the
          recurrent layer.
        lstm_size: An iterable of ints specifying the LSTM cell sizes to use.
        output_fc_layer_params: Optional list of fully connected parameters, where
          each item is the number of units in the layer. These are applied on top
          of the recurrent layer.
        activation_fn: Activation function, e.g. tf.keras.activations.relu,.
        rnn_construction_fn: (Optional.) Alternate RNN construction function, e.g.
          tf.keras.layers.LSTM, tf.keras.layers.CuDNNLSTM. It is invalid to
          provide both rnn_construction_fn and lstm_size.
        rnn_construction_kwargs: (Optional.) Dictionary or arguments to pass to
          rnn_construction_fn.  The RNN will be constructed via:  ``` rnn_layer =
          rnn_construction_fn(**rnn_construction_kwargs) ```
        dtype: The dtype to use by the convolution, LSTM, and fully connected
          layers.
        name: A string representing name of the network.
  
      Raises:
        ValueError: If any of `preprocessing_layers` is already built.
        ValueError: If `preprocessing_combiner` is already built.
        ValueError: If `action_spec` contains more than one action.
        ValueError: If neither `lstm_size` nor `rnn_construction_fn` are provided.
        ValueError: If both `lstm_size` and `rnn_construction_fn` are provided.
      """
      q_network.validate_specs(action_spec, input_tensor_spec)
      action_spec = tf.nest.flatten(action_spec)[0]
      num_actions = action_spec.maximum - action_spec.minimum + 1
  
      q_projection = layers.Dense(
          num_actions,
          activation=None,
          kernel_initializer=tf.random_uniform_initializer(
              minval=-0.03, maxval=0.03
          ),
          bias_initializer=tf.constant_initializer(-0.2),
          dtype=dtype,
          name="num_action_project/dense",
      )
  
      super(QRnnNetwork, self).__init__(
          input_tensor_spec=input_tensor_spec,
          preprocessing_layers=preprocessing_layers,
          preprocessing_combiner=preprocessing_combiner,
          conv_layer_params=conv_layer_params,
          input_fc_layer_params=input_fc_layer_params,
          lstm_size=lstm_size,
          output_fc_layer_params=output_fc_layer_params,
          activation_fn=activation_fn,
          rnn_construction_fn=rnn_construction_fn,
          rnn_construction_kwargs=rnn_construction_kwargs,
          dtype=dtype,
          name=name,
      )
  
      self._output_encoder.append(q_projection)
  
file_path: agents/tf_agents/networks/q_rnn_network.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gin
  import tensorflow
  from tensorflow.keras import layers
  from tf_agents.networks import lstm_encoding_network
  from tf_agents.networks import q_network
label_desc: Use image node_id 1 to create a new QRnnNetwork object from inherited base classes: lstm_encoding_network with example: obj = QRnnNetwork(input_tensor_spec, action_spec, preprocessing_layers, preprocessing_combiner, conv_layer_params, input_fc_layer_params, lstm_size, output_fc_layer_params, activation_fn, rnn_construction_fn, rnn_construction_kwargs, dtype, name)