tf_agents.bandits.agents.exp3_mixture_agent.Exp3MixtureAgent

An agent that mixes a set of agents and updates the weights with Exp3.

Inherits From: MixtureAgent

For a reference on EXP3, see Bandit Algorithms by Tor Lattimore and Csaba Szepesvari (https://tor-lattimore.com/downloads/book/book.pdf).

The update uses a slighlty modified version of EXP3 to make sure that the weights do not go to one seemingly good agent in the very beginning. To smooth the weights, two extra measures are taken:

  1. A forgetting factor makes sure that the aggregated reward estimates do not grow indefinitely.
  2. The inverse temperature has a maximum parameter that prevents it from growing indefinitely.

It is generally a good idea to set

forgetting_factor = 1 - (1 / max_inverse_temperature)

so that the two smoothing factors work together nicely.

For every data sample, the agent updates the sub-agent that was used to make the action choice in that sample. For this update to happen, the mixture agent needs to have the information on which sub-agent is "responsible" for the action. This information is in a policy info field mixture_choice_info.

agents List of TF-Agents agents that this mixture agent trains.
variable_collection An instance of Exp3VariableCollection. If not set, A default one will be created. It contains all the variables that are needed to restore the mixture agent, excluding the variables of the subagents.
forgetting A float value in (0, 1]. This is how much the estimated reward aggregates are shrinked in every training step.
max_inverse_temperature This value caps the inverse temperature that would otherwise grow as the square root of the number of samples seen.
name Name fo this instance of Exp3MixtureAgent.

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.
data_context

debug_summaries

policy Return the current policy held by the agent.
summaries_enabled

summarize_grads_and_vars

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.

train_step_counter

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

Methods

initialize

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Initializes the agent.

Returns
An operation that can be used to initialize the agent.

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

preprocess_sequence

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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.

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

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

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

train

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Trains the agent.

Args
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.

Returns
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.

Raises
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).