Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leiblier Maillard Sampling

• URL: http://arxiv.org/abs/2502.14379v1
• Date: Thu, 20 Feb 2025 09:12:16 GMT
• Title: Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leiblier Maillard Sampling
• Authors: Hao Qin, Kwang-Sung Jun, Chicheng Zhang,
• Abstract summary: We study the problem of Multi-Armed Bandits (MAB) with reward distributions belonging to a One- Exponential Distribution (OPED) family. In this paper, we design an algorithm, Exponential Kullback-Leibler Maillard Sampling (abbrev. expklms), that can achieve multiple optimality criteria simultaneously.
• Score: 24.487235945761913
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• Abstract: We study the problem of Multi-Armed Bandits (MAB) with reward distributions belonging to a One-Parameter Exponential Distribution (OPED) family. In the literature, several criteria have been proposed to evaluate the performance of such algorithms, including Asymptotic Optimality (A.O.), Minimax Optimality (M.O.), Sub-UCB, and variance-adaptive worst-case regret bound. Thompson Sampling (TS)-based and Upper Confidence Bound (UCB)-based algorithms have been employed to achieve some of these criteria. However, none of these algorithms simultaneously satisfy all the aforementioned criteria. In this paper, we design an algorithm, Exponential Kullback-Leibler Maillard Sampling (abbrev. \expklms), that can achieve multiple optimality criteria simultaneously, including A.O., M.O. with a logarithmic factor, Sub-UCB, and variance-adaptive worst-case regret bound.

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