Indexed Minimum Empirical Divergence for Unimodal BanditsDownload PDF

21 May 2021, 20:49 (modified: 28 Jan 2022, 13:36)NeurIPS 2021 PosterReaders: Everyone
Keywords: Multi-Armed Bandit, Indexed Minimum Empirical Divergence, Unimodal Bandits, Optimal Algorithm, One-Dimensional Exponential Family Distributions, Regret analysis
TL;DR: Optimal strategy for the unimodal bandit problem with one-dimentional family distributions. Elegant proof and improved practical performances.
Abstract: We consider a stochastic multi-armed bandit problem specified by a set of one-dimensional family exponential distributions endowed with a unimodal structure. The unimodal structure is of practical relevance for several applications. We introduce IMED-UB, an algorithm that exploits provably optimally 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 the IMED-UB algorithm, that is simple and yet yields asymptotic optimality. We finally provide numerical experiments showing that IMED-UB competes favorably with the recently introduced state-of-the-art algorithms.
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