A Note on KL-UCB+ Policy for the Stochastic Bandit

Abstract

A classic setting of the stochastic K-armed bandit problem is considered in this note. In this problem it has been known that KL-UCB policy achieves the asymptotically optimal regret bound and KL-UCB+ policy empirically performs better than the KL-UCB policy although the regret bound for the original form of the KL-UCB+ policy has been unknown. This note demonstrates that a simple proof of the asymptotic optimality of the KL-UCB+ policy can be given by the same technique as those used for analyses of other known policies.

arXiv comment6 pages, corrected typos

References in corpus (1)

• Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors

Cited by in corpus (1)

• Blind Exploration and Exploitation of Stochastic Experts