Thompson Sampling for Gaussian Entropic Risk Bandits

• URL: http://arxiv.org/abs/2105.06960v1
• Date: Fri, 14 May 2021 17:01:02 GMT
• Title: Thompson Sampling for Gaussian Entropic Risk Bandits
• Authors: Ming Liang Ang, Eloise Y. Y. Lim, Joel Q. L. Chang
• Abstract summary: Risk complicates the basic reward-maximising objectives, in part because there is no universally agreed definition of it. We consider an entropic risk (ER) measure and explore the performance of a Thompson sampling-based algorithm ERTS under this risk measure.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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