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We propose minimum empirical divergence (MED) policy for the multiarmedbandit problem. We prove asymptotic optimality of the proposed policy for thecase of finite support models. In our setting, Burnetas and Katehakis hasalready proposed an asymptotically optimal policy. For choosing an arm ourpolicy uses a criterion which is dual to the quantity used in Burnetas andKatehakis. Our criterion is easily computed by a convex optimization techniqueand has an advantage in practical implementation. We confirm by simulationsthat MED policy demonstrates good performance in finite time in comparison toother currently popular policies.

An Asymptotically Optimal Policy for Finite Support Models in the Multiarmed Bandit Problem