Periodica Mathematica Hungarica

, Volume 61, Issue 1–2, pp 55–65 | Cite as

UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem



In the stochastic multi-armed bandit problem we consider a modification of the UCB algorithm of Auer et al. [4]. For this modified algorithm we give an improved bound on the regret with respect to the optimal reward. While for the original UCB algorithm the regret in K-armed bandits after T trials is bounded by const · \( \frac{{K\log (T)}} {\Delta } \), where Δ measures the distance between a suboptimal arm and the optimal arm, for the modified UCB algorithm we show an upper bound on the regret of const · \( \frac{{K\log (T\Delta ^2 )}} {\Delta } \).

Key words and phrases

multi-armed bandit problem regret 

Mathematics subject classification numbers

68T05 62M05 91A60 


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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2010

Authors and Affiliations

  1. 1.Lehrstuhl für InformationstechnologieMontanuniversität LeobenLeobenAustria

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