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arxiv:2605.00488
Trading off rewards and errors in multi-armed bandits
Published on May 1
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Abstract
Multi-armed bandit algorithms balance accurate arm mean estimation and reward maximization through regret guarantees that interpolate between exploration and exploitation objectives.
AI-generated summary
In multi-armed bandits, the most-explored arms are the most informative, while reward maximization typically pulls only the best arm. We study the tradeoff between identifying arm means accurately and accumulating reward, and present an algorithm with regret guarantees that interpolates between the two objectives. We provide both upper and lower bounds and validate empirically.
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