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An agent that maintains linear reward estimates and their uncertainties.

Inherits From: TFAgent

    *args, **kwargs


  • exploration_policy: An Enum of type ExplorationPolicy. The kind of policy we use for exploration. Currently supported policies are LinUCBPolicy and LinearThompsonSamplingPolicy.
  • time_step_spec: A TimeStep spec describing the expected TimeSteps.
  • action_spec: A scalar BoundedTensorSpec with int32 or int64 dtype describing the number of actions for this agent.
  • variable_collection: Instance of LinearBanditVariableCollection. Collection of variables to be updated by the agent. If None, a new instance of LinearBanditVariableCollection will be created.
  • alpha: (float) positive scalar. This is the exploration parameter that multiplies the confidence intervals.
  • gamma: a float forgetting factor in [0.0, 1.0]. When set to 1.0, the algorithm does not forget.
  • use_eigendecomp: whether to use eigen-decomposition or not. The default solver is Conjugate Gradient.
  • tikhonov_weight: (float) tikhonov regularization term.
  • add_bias: If true, a bias term will be added to the linear reward estimation.
  • 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: Whether the policy emits log-probabilities or not. Since the policy is deterministic, the probability is just 1.
  • 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.
  • 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.
  • dtype: The type of the parameters stored and updated by the agent. Should be one of tf.float32 and tf.float64. Defaults to tf.float32.
  • name: a name for this instance of LinearBanditAgent.


  • action_spec: TensorSpec describing the action produced by the agent.

  • alpha

  • collect_data_spec: Returns a Trajectory spec, as expected by the collect_policy.

  • collect_policy: Return a policy that can be used to collect data from the environment.

  • cov_matrix

  • data_vector

  • debug_summaries

  • eig_matrix

  • eig_vals

  • name: Returns the name of this module as passed or determined in the ctor.

    NOTE: This is not the same as the self.name_scope.name which includes parent module names.

  • name_scope: Returns a tf.name_scope instance for this class.

  • num_actions

  • num_samples

  • policy: Return the current policy held by the agent.

  • submodules: Sequence of all sub-modules.

    Submodules are modules which are properties of this module, or found as properties of modules which are properties of this module (and so on).

a = tf.Module()
b = tf.Module()
c = tf.Module()
a.b = b
b.c = c
assert list(a.submodules) == [b, c]
assert list(b.submodules) == [c]
assert list(c.submodules) == []
  • summaries_enabled
  • summarize_grads_and_vars
  • theta: Returns the matrix of per-arm feature weights.

    The returned matrix has shape (num_actions, context_dim). It's equivalent to a stacking of theta vectors from the paper.

  • time_step_spec: Describes the TimeStep tensors expected by the agent.

  • train_sequence_length: The number of time steps needed in experience tensors passed to train.

    Train requires experience to be a Trajectory containing tensors shaped [B, T, ...]. This argument describes the value of T required.

    For example, for non-RNN DQN training, T=2 because DQN requires single transitions.

    If this value is None, then train can handle an unknown T (it can be determined at runtime from the data). Most RNN-based agents fall into this category.

  • train_step_counter

  • trainable_variables: Sequence of trainable variables owned by this module and its submodules.

  • variables: Sequence of variables owned by this module and its submodules.


ValueError if dtype is not one of tf.float32 or tf.float64. TypeError if variable_collection is not an instance of LinearBanditVariableCollection.



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Initializes the agent.


An operation that can be used to initialize the agent.


  • RuntimeError: If the class was not initialized properly (super.__init__ was not called).


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    experience, weights=None

Trains the agent.


  • experience: A batch of experience data in the form of a Trajectory. The structure of experience must match that of self.collect_data_spec. All tensors in experience must be shaped [batch, time, ...] where time must be equal to self.train_step_length if that property is not None.
  • weights: (optional). A Tensor, either 0-D or shaped [batch], containing weights to be used when calculating the total train loss. Weights are typically multiplied elementwise against the per-batch loss, but the implementation is up to the Agent.


A LossInfo loss tuple containing loss and info tensors.

  • In eager mode, the loss values are first calculated, then a train step is performed before they are returned.
  • In graph mode, executing any or all of the loss tensors will first calculate the loss value(s), then perform a train step, and return the pre-train-step LossInfo.


  • TypeError: If experience is not type Trajectory. Or if experience does not match self.collect_data_spec structure types.
  • ValueError: If experience tensors' time axes are not compatible with self.train_sequence_length. Or if experience does not match self.collect_data_spec structure.
  • RuntimeError: If the class was not initialized properly (super.__init__ was not called).


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    cls, method

Decorator to automatically enter the module name scope.

class MyModule(tf.Module):
  def __call__(self, x):
    if not hasattr(self, 'w'):
      self.w = tf.Variable(tf.random.normal([x.shape[1], 64]))
    return tf.matmul(x, self.w)

Using the above module would produce tf.Variables and tf.Tensors whose names included the module name:

mod = MyModule()
mod(tf.ones([8, 32]))
# ==> <tf.Tensor: ...>
# ==> <tf.Variable ...'my_module/w:0'>


  • method: The method to wrap.


The original method wrapped such that it enters the module's name scope.