class_docstr: Creates an actor producing either Normal or Categorical distribution.
  
  Note: By default, this network uses `NormalProjectionNetwork` for continuous
  projection which by default uses `tanh_squash_to_spec` to normalize its
  output. Due to the nature of the `tanh` function, values near the spec bounds
  cannot be returned.
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docstr: Creates an instance of `ActorDistributionNetwork`.
  
  Args:
    input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
      input.
    output_tensor_spec: A nest of `tensor_spec.BoundedTensorSpec` representing
      the output.
    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.nn.relu, slim.leaky_relu, ...
    kernel_initializer: Initializer to use for the kernels of the conv and
      dense layers. If none is provided a default glorot_uniform.
    seed_stream_class: The seed stream class. This is almost always
      tfp.util.SeedStream, except for in unit testing, when one may want to
      seed all the layers deterministically.
    seed: seed used for Keras kernal initializers for NormalProjectionNetwork.
    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.
    discrete_projection_net: Callable that generates a discrete projection
      network to be called with some hidden state and the outer_rank of the
      state.
    continuous_projection_net: Callable that generates a continuous projection
      network to be called with some hidden state and the outer_rank of the
      state.
    name: A string representing name of the network.
  
  Raises:
    ValueError: If `input_tensor_spec` contains more than one observation.
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code: def __init__(
      self,
      input_tensor_spec,
      output_tensor_spec,
      preprocessing_layers=None,
      preprocessing_combiner=None,
      conv_layer_params=None,
      fc_layer_params=(200, 100),
      dropout_layer_params=None,
      activation_fn=tf.keras.activations.relu,
      kernel_initializer=None,
      seed_stream_class=tfp.util.SeedStream,
      seed=None,
      batch_squash=True,
      dtype=tf.float32,
      discrete_projection_net=_categorical_projection_net,
      continuous_projection_net=_normal_projection_net,
      name="ActorDistributionNetwork",
  ):
      """Creates an instance of `ActorDistributionNetwork`.
  
      Args:
        input_tensor_spec: A nest of `tensor_spec.TensorSpec` representing the
          input.
        output_tensor_spec: A nest of `tensor_spec.BoundedTensorSpec` representing
          the output.
        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.nn.relu, slim.leaky_relu, ...
        kernel_initializer: Initializer to use for the kernels of the conv and
          dense layers. If none is provided a default glorot_uniform.
        seed_stream_class: The seed stream class. This is almost always
          tfp.util.SeedStream, except for in unit testing, when one may want to
          seed all the layers deterministically.
        seed: seed used for Keras kernal initializers for NormalProjectionNetwork.
        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.
        discrete_projection_net: Callable that generates a discrete projection
          network to be called with some hidden state and the outer_rank of the
          state.
        continuous_projection_net: Callable that generates a continuous projection
          network to be called with some hidden state and the outer_rank of the
          state.
        name: A string representing name of the network.
  
      Raises:
        ValueError: If `input_tensor_spec` contains more than one observation.
      """
  
      if not kernel_initializer:
          kernel_initializer = (
              tf.compat.v1.keras.initializers.glorot_uniform()
          )
  
      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,
      )
  
      def map_proj(spec):
          if tensor_spec.is_discrete(spec):
              return discrete_projection_net(spec)
          else:
              kwargs = {}
              if continuous_projection_net is _normal_projection_net:
                  kwargs["seed"] = seed
                  kwargs["seed_stream_class"] = seed_stream_class
              return continuous_projection_net(spec, **kwargs)
  
      projection_networks = tf.nest.map_structure(
          map_proj, output_tensor_spec
      )
      output_spec = tf.nest.map_structure(
          lambda proj_net: proj_net.output_spec, projection_networks
      )
  
      super(ActorDistributionNetwork, self).__init__(
          input_tensor_spec=input_tensor_spec,
          state_spec=(),
          output_spec=output_spec,
          name=name,
      )
  
      self._encoder = encoder
      self._projection_networks = projection_networks
      self._output_tensor_spec = output_tensor_spec
  
file_path: agents/tf_agents/networks/actor_distribution_network.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gin
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.networks import categorical_projection_network
  from tf_agents.networks import encoding_network
  from tf_agents.networks import network
  from tf_agents.networks import normal_projection_network
  from tf_agents.specs import tensor_spec
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 1 to create a new ActorDistributionNetwork object from inherited base classes: network with example: obj = ActorDistributionNetwork(input_tensor_spec, output_tensor_spec, preprocessing_layers, preprocessing_combiner, conv_layer_params, fc_layer_params, dropout_layer_params, activation_fn, kernel_initializer, seed_stream_class, seed, batch_squash, dtype, discrete_projection_net, continuous_projection_net, name)