class_docstr: Neural LinUCB Policy.
  
  Applies LinUCB on top of an encoding network.
  Since LinUCB is a linear method, the encoding network is used to capture the
  non-linear relationship between the context features and the expected rewards.
  The policy starts with exploration based on epsilon greedy and then switches
  to LinUCB for exploring more efficiently.
  
  This policy supports both the global-only observation model and the global and
  per-arm model:
  
  -- In the global-only case, there is one single observation per
     time step, and every arm has its own reward estimation function.
  -- In the per-arm case, all arms receive individual observations, and the
     reward estimation function is identical for all arms.
  
  Reference:
  Carlos Riquelme, George Tucker, Jasper Snoek,
  `Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep
  Networks for Thompson Sampling`, ICLR 2018.
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docstr: Initializes `NeuralLinUCBPolicy`.
  
  Args:
    encoding_network: network that encodes the observations.
    encoding_dim: (int) dimension of the encoded observations.
    reward_layer: final layer that predicts the expected reward per arm. In
      case the policy accepts per-arm features, the output of this layer has
      to be a scalar. This is because in the per-arm case, all encoded
      observations have to go through the same computation to get the reward
      estimates. The `num_actions` dimension of the encoded observation is
      treated as a batch dimension in the reward layer.
    epsilon_greedy: (float) representing the probability of choosing a random
      action instead of the greedy action.
    actions_from_reward_layer: (boolean variable) whether to get actions from
      the reward layer or from LinUCB.
    cov_matrix: list of the covariance matrices. There exists one covariance
      matrix per arm, unless the policy accepts per-arm features, in which
      case this list must have a single element.
    data_vector: list of the data vectors. A data vector is a weighted sum of
      the observations, where the weight is the corresponding reward. Each arm
      has its own data vector, unless the policy accepts per-arm features, in
      which case this list must have a single element.
    num_samples: list of number of samples per arm. If the policy accepts per-
      arm features, this is a single-element list counting the number of
      steps.
    time_step_spec: A `TimeStep` spec of the expected time_steps.
    alpha: (float) non-negative weight multiplying the confidence intervals.
    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`.
    emit_log_probability: (bool) whether to emit log probabilities.
    accepts_per_arm_features: (bool) Whether the policy accepts per-arm
      features.
    distributed_use_reward_layer: (bool) Whether to pick the actions using the
      network or use LinUCB. This applies only in distributed training setting
      and has a similar role to the `actions_from_reward_layer` mentioned
      above.
    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 policy and 2)
      the mask. The mask should be a 0-1 `Tensor` of shape `[batch_size,
      num_actions]`. This function should also work with a `TensorSpec` as
      input, and should output `TensorSpec` objects for the observation and
      mask.
    name: The name of this policy.
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code: def __init__(
      self,
      encoding_network: types.Network,
      encoding_dim: int,
      reward_layer: tf.keras.layers.Dense,
      epsilon_greedy: float,
      actions_from_reward_layer: types.Bool,
      cov_matrix: Sequence[types.Float],
      data_vector: Sequence[types.Float],
      num_samples: Sequence[types.Int],
      time_step_spec: types.TimeStep,
      alpha: float = 1.0,
      emit_policy_info: Sequence[Text] = (),
      emit_log_probability: bool = False,
      accepts_per_arm_features: bool = False,
      distributed_use_reward_layer: bool = False,
      observation_and_action_constraint_splitter: Optional[
          types.Splitter
      ] = None,
      name: Optional[Text] = None,
  ):
      """Initializes `NeuralLinUCBPolicy`.
  
      Args:
        encoding_network: network that encodes the observations.
        encoding_dim: (int) dimension of the encoded observations.
        reward_layer: final layer that predicts the expected reward per arm. In
          case the policy accepts per-arm features, the output of this layer has
          to be a scalar. This is because in the per-arm case, all encoded
          observations have to go through the same computation to get the reward
          estimates. The `num_actions` dimension of the encoded observation is
          treated as a batch dimension in the reward layer.
        epsilon_greedy: (float) representing the probability of choosing a random
          action instead of the greedy action.
        actions_from_reward_layer: (boolean variable) whether to get actions from
          the reward layer or from LinUCB.
        cov_matrix: list of the covariance matrices. There exists one covariance
          matrix per arm, unless the policy accepts per-arm features, in which
          case this list must have a single element.
        data_vector: list of the data vectors. A data vector is a weighted sum of
          the observations, where the weight is the corresponding reward. Each arm
          has its own data vector, unless the policy accepts per-arm features, in
          which case this list must have a single element.
        num_samples: list of number of samples per arm. If the policy accepts per-
          arm features, this is a single-element list counting the number of
          steps.
        time_step_spec: A `TimeStep` spec of the expected time_steps.
        alpha: (float) non-negative weight multiplying the confidence intervals.
        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`.
        emit_log_probability: (bool) whether to emit log probabilities.
        accepts_per_arm_features: (bool) Whether the policy accepts per-arm
          features.
        distributed_use_reward_layer: (bool) Whether to pick the actions using the
          network or use LinUCB. This applies only in distributed training setting
          and has a similar role to the `actions_from_reward_layer` mentioned
          above.
        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 policy and 2)
          the mask. The mask should be a 0-1 `Tensor` of shape `[batch_size,
          num_actions]`. This function should also work with a `TensorSpec` as
          input, and should output `TensorSpec` objects for the observation and
          mask.
        name: The name of this policy.
      """
      policy_utilities.check_no_mask_with_arm_features(
          accepts_per_arm_features,
          observation_and_action_constraint_splitter,
      )
      encoding_network.create_variables()
      self._encoding_network = encoding_network
      self._reward_layer = reward_layer
      self._encoding_dim = encoding_dim
  
      if accepts_per_arm_features and reward_layer.units != 1:
          raise ValueError(
              "The output dimension of the reward layer must be 1, got {}".format(
                  reward_layer.units
              )
          )
  
      if not isinstance(cov_matrix, (list, tuple)):
          raise ValueError(
              "cov_matrix must be a list of matrices (Tensors)."
          )
      self._cov_matrix = cov_matrix
  
      if not isinstance(data_vector, (list, tuple)):
          raise ValueError(
              "data_vector must be a list of vectors (Tensors)."
          )
      self._data_vector = data_vector
  
      if not isinstance(num_samples, (list, tuple)):
          raise ValueError(
              "num_samples must be a list of vectors (Tensors)."
          )
      self._num_samples = num_samples
  
      self._alpha = alpha
      self._actions_from_reward_layer = actions_from_reward_layer
      self._epsilon_greedy = epsilon_greedy
      self._dtype = self._data_vector[0].dtype
      self._distributed_use_reward_layer = distributed_use_reward_layer
  
      if len(cov_matrix) != len(data_vector):
          raise ValueError(
              "The size of list cov_matrix must match the size of "
              "list data_vector. Got {} for cov_matrix and {} "
              "for data_vector".format(
                  len(self._cov_matrix), len((data_vector))
              )
          )
      if len(num_samples) != len(cov_matrix):
          raise ValueError(
              "The size of num_samples must match the size of "
              "list cov_matrix. Got {} for num_samples and {} "
              "for cov_matrix".format(
                  len(self._num_samples), len((cov_matrix))
              )
          )
  
      self._accepts_per_arm_features = accepts_per_arm_features
      if observation_and_action_constraint_splitter is not None:
          context_spec, _ = observation_and_action_constraint_splitter(
              time_step_spec.observation
          )
      else:
          context_spec = time_step_spec.observation
      if accepts_per_arm_features:
          self._num_actions = tf.nest.flatten(
              context_spec[bandit_spec_utils.PER_ARM_FEATURE_KEY]
          )[0].shape.as_list()[0]
          self._num_models = 1
      else:
          self._num_actions = len(cov_matrix)
          self._num_models = self._num_actions
      cov_matrix_dim = tf.compat.dimension_value(cov_matrix[0].shape[0])
      if self._encoding_dim != cov_matrix_dim:
          raise ValueError(
              "The dimension of matrix `cov_matrix` must match "
              "encoding dimension {}."
              "Got {} for `cov_matrix`.".format(
                  self._encoding_dim, cov_matrix_dim
              )
          )
      data_vector_dim = tf.compat.dimension_value(
          data_vector[0].shape[0]
      )
      if self._encoding_dim != data_vector_dim:
          raise ValueError(
              "The dimension of vector `data_vector` must match "
              "encoding  dimension {}. "
              "Got {} for `data_vector`.".format(
                  self._encoding_dim, data_vector_dim
              )
          )
      action_spec = tensor_spec.BoundedTensorSpec(
          shape=(),
          dtype=tf.int32,
          minimum=0,
          maximum=self._num_actions - 1,
          name="action",
      )
  
      self._emit_policy_info = emit_policy_info
      predicted_rewards_mean = ()
      if (
          policy_utilities.InfoFields.PREDICTED_REWARDS_MEAN
          in emit_policy_info
      ):
          predicted_rewards_mean = tensor_spec.TensorSpec(
              [self._num_actions], dtype=tf.float32
          )
      predicted_rewards_optimistic = ()
      if (
          policy_utilities.InfoFields.PREDICTED_REWARDS_OPTIMISTIC
          in emit_policy_info
      ):
          predicted_rewards_optimistic = tensor_spec.TensorSpec(
              [self._num_actions], dtype=tf.float32
          )
      if accepts_per_arm_features:
          chosen_arm_features_info_spec = (
              policy_utilities.create_chosen_arm_features_info_spec(
                  time_step_spec.observation
              )
          )
          info_spec = policy_utilities.PerArmPolicyInfo(
              predicted_rewards_mean=predicted_rewards_mean,
              predicted_rewards_optimistic=predicted_rewards_optimistic,
              chosen_arm_features=chosen_arm_features_info_spec,
          )
      else:
          info_spec = policy_utilities.PolicyInfo(
              predicted_rewards_mean=predicted_rewards_mean,
              predicted_rewards_optimistic=predicted_rewards_optimistic,
          )
  
      super(NeuralLinUCBPolicy, self).__init__(
          time_step_spec=time_step_spec,
          action_spec=action_spec,
          emit_log_probability=emit_log_probability,
          observation_and_action_constraint_splitter=(
              observation_and_action_constraint_splitter
          ),
          info_spec=info_spec,
          name=name,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Optional, Sequence, Text, Tuple
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.policies import constraints
  from tf_agents.bandits.policies import linalg
  from tf_agents.bandits.specs import utils
  from tf_agents.distributions import masked
  from tf_agents.policies import tf_policy
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.typing import types
label_desc: Use image node_id 1 to create a new NeuralLinUCBPolicy object from inherited base classes: tf_policy with example: obj = NeuralLinUCBPolicy(encoding_network, encoding_dim, reward_layer, epsilon_greedy, actions_from_reward_layer, cov_matrix, data_vector, num_samples, time_step_spec, alpha, emit_policy_info, emit_log_probability, accepts_per_arm_features, distributed_use_reward_layer, observation_and_action_constraint_splitter, name)