An agent implementing the EXP3 bandit algorithm.

Inherits From: TFAgent

Implementation based on

"Bandit Algorithms" Lattimore and Szepesvari, 2019

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.
learning_rate A float valued scalar. A higher value will force the agent to converge on a single action more quickly. A lower value will encourage more exploration. This value corresponds to the inverse_temperature argument passed to CategoricalPolicy.
name a name for this instance of Exp3Agent.

action_spec TensorSpec describing the action produced by the agent.
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.




policy Return the current policy held by the agent.


time_step_spec Describes the TimeStep tensors expected by the agent.
train_argspec TensorSpec describing extra supported kwargs to train().
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.


training_data_spec Returns a trajectory spec, as expected by the train() function.
validate_args Whether train & preprocess_sequence validate input & output args.



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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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Defines preprocess_sequence function to be fed into replay buffers.

This defines how we preprocess the collected data before training. Defaults to pass through for most agents. Structure of experience must match that of self.collect_data_spec.

experience a Trajectory shaped [batch, time, ...] or [time, ...] which represents the collected experience data.

A post processed Trajectory with the same shape as the input.

TypeError If experience does not match self.collect_data_spec structure types.


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Trains the agent.

experience A batch of experience data in the form of a Trajectory. The structure of experience must match that of self.training_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.
**kwargs Any additional data as declared by self.train_argspec.

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 validate_args is True and: Experience is not type Trajectory; or if experience does not match self.training_data_spec structure types.
ValueError If validate_args is True and: Experience tensors' time axes are not compatible with self.train_sequence_length; or if experience does not match self.training_data_spec structure.
ValueError If validate_args is True and the user does not pass **kwargs matching self.train_argspec.
RuntimeError If the class was not initialized properly (super.__init__ was not called).