class_docstr: A neural network based Thompson sampling agent.
  
  This agent receives parameters for a neural network and trains it to predict
  rewards. The action is chosen greedily with respect to the prediction.
  The neural network implements dropout for exploration.
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docstr: Creates a Dropout Thompson Sampling Agent.
  
  For more details about the Laplacian smoothing regularization, please see
  the documentation of the `GreedyRewardPredictionAgent`.
  
  Args:
    time_step_spec: A `TimeStep` spec of the expected time_steps.
    action_spec: A nest of `BoundedTensorSpec` representing the actions.
    optimizer: The optimizer to use for training.
    dropout_rate: Float in `(0, 1)`, or a function that takes no arguments and
      returns a float in `(0,1)`. It represents the dropout rate used by the
      model.
    network_layers: Tuple of ints determining the sizes of the network layers.
    dropout_only_top_layer: Boolean parameter determining if dropout should be
      done only in the top layer. True by default.
    observation_and_action_constraint_splitter: A function used for masking
      valid/invalid actions with each state of the environment. The function
      takes in a full observation and returns a tuple consisting of 1) the
      part of the observation intended as input to the bandit agent and
      policy, and 2) the boolean mask. This function should also work with a
      `TensorSpec` as input, and should output `TensorSpec` objects for the
      observation and mask.
    constraints: iterable of constraints objects that are instances of
      `tf_agents.bandits.agents.NeuralConstraint`.
    error_loss_fn: A function for computing the error loss, taking parameters
      labels, predictions, and weights (any function from tf.losses would
      work). The default is `tf.losses.mean_squared_error`.
    gradient_clipping: A float representing the norm length to clip gradients
      (or None for no clipping.)
    heteroscedastic: If True, the variance per action is estimated and the
      losses are weighted appropriately.
    debug_summaries: A Python bool, default False. When True, debug summaries
      are gathered.
    summarize_grads_and_vars: A Python bool, default False. When True,
      gradients and network variable summaries are written during training.
    enable_summaries: A Python bool, default True. When False, all summaries
      (debug or otherwise) should not be written.
    emit_policy_info: (tuple of strings) what side information we want to get
      as part of the policy info. Allowed values can be found in
      `policy_utilities.PolicyInfo`.
    train_step_counter: An optional `tf.Variable` to increment every time the
      train op is run.  Defaults to the `global_step`.
    laplacian_matrix: A float `Tensor` shaped `[num_actions, num_actions]`.
      This holds the Laplacian matrix used to regularize the smoothness of the
      estimated expected reward function. This only applies to problems where
      the actions have a graph structure. If `None`, the regularization is not
      applied.
    laplacian_smoothing_weight: A float that determines the weight of the
      regularization term. Note that this has no effect if `laplacian_matrix`
      above is `None`.
    name: Python str name of this agent. All variables in this module will
      fall under that name. Defaults to the class name.
  
  Raises:
    ValueError: If the action spec contains more than one action or or it is
    not a bounded scalar int32 spec with minimum 0.
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code: def __init__(
      self,
      time_step_spec: types.TimeStep,
      action_spec: types.BoundedTensorSpec,
      optimizer: types.Optimizer,
      # Network params.
      dropout_rate: types.FloatOrReturningFloat,
      network_layers: Tuple[int, ...],
      dropout_only_top_layer: bool = True,
      observation_and_action_constraint_splitter: Optional[
          types.Splitter
      ] = None,
      constraints: Iterable[constr.BaseConstraint] = (),
      # Params for training.
      error_loss_fn: types.LossFn = tf.compat.v1.losses.mean_squared_error,
      gradient_clipping: Optional[float] = None,
      heteroscedastic: bool = False,
      # Params for debugging.
      debug_summaries: bool = False,
      summarize_grads_and_vars: bool = False,
      enable_summaries: bool = True,
      emit_policy_info: Tuple[Text, ...] = (),
      train_step_counter: Optional[tf.Variable] = None,
      laplacian_matrix: Optional[types.Float] = None,
      laplacian_smoothing_weight: float = 0.001,
      name: Optional[Text] = None,
  ):
      """Creates a Dropout Thompson Sampling Agent.
  
      For more details about the Laplacian smoothing regularization, please see
      the documentation of the `GreedyRewardPredictionAgent`.
  
      Args:
        time_step_spec: A `TimeStep` spec of the expected time_steps.
        action_spec: A nest of `BoundedTensorSpec` representing the actions.
        optimizer: The optimizer to use for training.
        dropout_rate: Float in `(0, 1)`, or a function that takes no arguments and
          returns a float in `(0,1)`. It represents the dropout rate used by the
          model.
        network_layers: Tuple of ints determining the sizes of the network layers.
        dropout_only_top_layer: Boolean parameter determining if dropout should be
          done only in the top layer. True by default.
        observation_and_action_constraint_splitter: A function used for masking
          valid/invalid actions with each state of the environment. The function
          takes in a full observation and returns a tuple consisting of 1) the
          part of the observation intended as input to the bandit agent and
          policy, and 2) the boolean mask. This function should also work with a
          `TensorSpec` as input, and should output `TensorSpec` objects for the
          observation and mask.
        constraints: iterable of constraints objects that are instances of
          `tf_agents.bandits.agents.NeuralConstraint`.
        error_loss_fn: A function for computing the error loss, taking parameters
          labels, predictions, and weights (any function from tf.losses would
          work). The default is `tf.losses.mean_squared_error`.
        gradient_clipping: A float representing the norm length to clip gradients
          (or None for no clipping.)
        heteroscedastic: If True, the variance per action is estimated and the
          losses are weighted appropriately.
        debug_summaries: A Python bool, default False. When True, debug summaries
          are gathered.
        summarize_grads_and_vars: A Python bool, default False. When True,
          gradients and network variable summaries are written during training.
        enable_summaries: A Python bool, default True. When False, all summaries
          (debug or otherwise) should not be written.
        emit_policy_info: (tuple of strings) what side information we want to get
          as part of the policy info. Allowed values can be found in
          `policy_utilities.PolicyInfo`.
        train_step_counter: An optional `tf.Variable` to increment every time the
          train op is run.  Defaults to the `global_step`.
        laplacian_matrix: A float `Tensor` shaped `[num_actions, num_actions]`.
          This holds the Laplacian matrix used to regularize the smoothness of the
          estimated expected reward function. This only applies to problems where
          the actions have a graph structure. If `None`, the regularization is not
          applied.
        laplacian_smoothing_weight: A float that determines the weight of the
          regularization term. Note that this has no effect if `laplacian_matrix`
          above is `None`.
        name: Python str name of this agent. All variables in this module will
          fall under that name. Defaults to the class name.
  
      Raises:
        ValueError: If the action spec contains more than one action or or it is
        not a bounded scalar int32 spec with minimum 0.
      """
      self._dropout_rate = dropout_rate
      fc_layer_params = network_layers
      dropout_param = {"rate": dropout_rate, "permanent": True}
      if dropout_only_top_layer:
          dropout_layer_params = [None] * (len(fc_layer_params) - 1)
          dropout_layer_params.append(dropout_param)
      else:
          dropout_layer_params = [dropout_param] * len(fc_layer_params)
      if observation_and_action_constraint_splitter is not None:
          (
              input_tensor_spec,
              _,
          ) = observation_and_action_constraint_splitter(
              time_step_spec.observation
          )
      else:
          input_tensor_spec = time_step_spec.observation
  
      if heteroscedastic:
          reward_network = (
              heteroscedastic_q_network.HeteroscedasticQNetwork(
                  input_tensor_spec=input_tensor_spec,
                  action_spec=action_spec,
                  fc_layer_params=fc_layer_params,
                  dropout_layer_params=dropout_layer_params,
              )
          )
      else:
          reward_network = q_network.QNetwork(
              input_tensor_spec=input_tensor_spec,
              action_spec=action_spec,
              fc_layer_params=fc_layer_params,
              dropout_layer_params=dropout_layer_params,
          )
  
      super(DropoutThompsonSamplingAgent, self).__init__(
          time_step_spec=time_step_spec,
          action_spec=action_spec,
          reward_network=reward_network,
          optimizer=optimizer,
          observation_and_action_constraint_splitter=(
              observation_and_action_constraint_splitter
          ),
          constraints=constraints,
          error_loss_fn=error_loss_fn,
          gradient_clipping=gradient_clipping,
          debug_summaries=debug_summaries,
          summarize_grads_and_vars=summarize_grads_and_vars,
          enable_summaries=enable_summaries,
          emit_policy_info=emit_policy_info,
          train_step_counter=train_step_counter,
          laplacian_matrix=laplacian_matrix,
          laplacian_smoothing_weight=laplacian_smoothing_weight,
          name=name,
      )
  
file_path: agents/tf_agents/bandits/agents/dropout_thompson_sampling_agent.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Iterable, Optional, Text, Tuple
  import gin
  import tensorflow
  from tf_agents.bandits.agents import greedy_reward_prediction_agent
  from tf_agents.bandits.networks import heteroscedastic_q_network
  from tf_agents.bandits.policies import constraints
  from tf_agents.networks import q_network
  from tf_agents.typing import types
label_desc: Use image node_id 1 to create a new DropoutThompsonSamplingAgent object from inherited base classes: greedy_reward_prediction_agent with example: obj = DropoutThompsonSamplingAgent(time_step_spec, action_spec, optimizer, dropout_rate, network_layers, dropout_only_top_layer, observation_and_action_constraint_splitter, constraints, error_loss_fn, gradient_clipping, heteroscedastic, debug_summaries, summarize_grads_and_vars, enable_summaries, emit_policy_info, train_step_counter, laplacian_matrix, laplacian_smoothing_weight, name)