Statistics > Machine Learning
[Submitted on 19 Mar 2022 (v1), last revised 25 Mar 2022 (this version, v2)]
Title:Thompson Sampling on Asymmetric $α$-Stable Bandits
View PDFAbstract:In algorithm optimization in reinforcement learning, how to deal with the exploration-exploitation dilemma is particularly important. Multi-armed bandit problem can optimize the proposed solutions by changing the reward distribution to realize the dynamic balance between exploration and exploitation. Thompson Sampling is a common method for solving multi-armed bandit problem and has been used to explore data that conform to various laws. In this paper, we consider the Thompson Sampling approach for multi-armed bandit problem, in which rewards conform to unknown asymmetric $\alpha$-stable distributions and explore their applications in modelling financial and wireless data.
Submission history
From: Zhendong Shi [view email][v1] Sat, 19 Mar 2022 01:55:08 UTC (272 KB)
[v2] Fri, 25 Mar 2022 13:59:55 UTC (273 KB)
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