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arXiv · 2105.06960

Thompson Sampling for Gaussian Entropic Risk Bandits

Ming Liang Ang·Eloise Y. Y. Lim·Joel Q. L. Chang
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Abstract

The multi-armed bandit (MAB) problem is a ubiquitous decision-making problem that exemplifies exploration-exploitation tradeoff. Standard formulations exclude risk in decision making. Risknotably complicates the basic reward-maximising objectives, in part because there is no universally agreed definition of it. In this paper, we consider an entropic risk (ER) measure and explore the performance of a Thompson sampling-based algorithm ERTS under this risk measure by providing regret bounds for ERTS and corresponding instance dependent lower bounds.

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Ming Liang Ang, Eloise Y. Y. Lim, Joel Q. L. Chang. 2021-05-14. Thompson Sampling for Gaussian Entropic Risk Bandits. https://arxiv.org/abs/2105.06960

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