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arXiv · 1903.07839

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

Junya Honda
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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.

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Junya Honda. 2019-03-19. A Note on KL-UCB+ Policy for the Stochastic Bandit. https://arxiv.org/abs/1903.07839

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