Hyper-parameter Tuning for the Contextual Bandit

TL;DR

This paper introduces two algorithms that automatically learn optimal exploration parameters in contextual bandit problems with linear rewards, aiming to automate the tuning process and improve decision-making efficiency.

Contribution

The paper proposes the first algorithms that adaptively learn exploration parameters online in contextual bandits, reducing manual tuning and enhancing performance.

Findings

Algorithms effectively learn exploration parameters online

Improved decision-making in contextual bandit tasks

Potential for automated bandit algorithm tuning

Abstract

We study here the problem of learning the exploration exploitation trade-off in the contextual bandit problem with linear reward function setting. In the traditional algorithms that solve the contextual bandit problem, the exploration is a parameter that is tuned by the user. However, our proposed algorithm learn to choose the right exploration parameters in an online manner based on the observed context, and the immediate reward received for the chosen action. We have presented here two algorithms that uses a bandit to find the optimal exploration of the contextual bandit algorithm, which we hope is the first step toward the automation of the multi-armed bandit algorithm.