The K-armed dueling bandits problem (original) (raw)

Abstract

We study a partial-information online-learning problem where actions are restricted to noisy comparisons between pairs of strategies (also known as bandits). In contrast to conventional approaches that require the absolute reward of the chosen strategy to be quantifiable and observable, our setting assumes only that (noisy) binary feedback about the relative reward of two chosen strategies is available. This type of relative feedback is particularly appropriate in applications where absolute rewards have no natural scale or are difficult to measure (e.g., user-perceived quality of a set of retrieval results, taste of food, product attractiveness), but where pairwise comparisons are easy to make. We propose a novel regret formulation in this setting, as well as present an algorithm that achieves information-theoretically optimal regret bounds (up to a constant factor).

Highlights

► We study a bandit setting where the only feedback is the result of comparing pairs of arms. ► We formulate a regret minimization objective for this setting. ► We develop an algorithm for this setting and prove that it is optimal (up to constant factors).

Keywords

Online learning

Multi-armed bandits

Preference elicitation

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