Indexed Minimum Empirical Divergence for Unimodal Bandits

We consider a multi-armed bandit problem specified by a set of one-dimensional family exponential distributions endowed with a unimodal structure. We introduce IMED-UB, a algorithm that optimally exploits the unimodal-structure, by adapting to this setting the Indexed Minimum Empirical Divergence (I...

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Bibliographic Details
Main Authors Saber, Hassan, Ménard, Pierre, Maillard, Odalric-Ambrym
Format Journal Article
LanguageEnglish
Published 02.12.2021
Subjects
Computer Science - Artificial Intelligence
Computer Science - Learning
Mathematics - Optimization and Control
Statistics - Machine Learning
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Summary:We consider a multi-armed bandit problem specified by a set of one-dimensional family exponential distributions endowed with a unimodal structure. We introduce IMED-UB, a algorithm that optimally exploits the unimodal-structure, by adapting to this setting the Indexed Minimum Empirical Divergence (IMED) algorithm introduced by Honda and Takemura [2015]. Owing to our proof technique, we are able to provide a concise finite-time analysis of IMED-UB algorithm. Numerical experiments show that IMED-UB competes with the state-of-the-art algorithms.
DOI:10.48550/arxiv.2112.01452