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Multi-armed bandit (MAB) is a class of online learning problems where alearning agent aims to maximize its expected cumulative reward while repeatedlyselecting to pull arms with unknown reward distributions. We consider ascenario where the reward distributions may change in a piecewise-stationaryfashion at unknown time steps. We show that by incorporating a simplechange-detection component with classic UCB algorithms to detect and adapt tochanges, our so-called M-UCB algorithm can achieve nearly optimal regret boundon the order of $O(\sqrt{MKT\log T})$, where $T$ is the number of time steps,$K$ is the number of arms, and $M$ is the number of stationary segments.Comparison with the best available lower bound shows that our M-UCB is nearlyoptimal in $T$ up to a logarithmic factor. We also compare M-UCB with thestate-of-the-art algorithms in numerical experiments using a public Yahoo!dataset to demonstrate its superior performance.

Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit