Abstract
We consider the two-armed bandit problem as applied to data processing if there are two alternative processing methods available with different a priori unknown efficiencies. On should determine the most effective method and provide its dominating application. The total number of data, which is interpreted as a control horizon, is assumed to have a priori known probability distribution.The problem is considered in minimax (robust) setting. According to the main theorem of the theory of games minimax risk and minimax strategy are sought for as Bayesian ones corresponding to the worst-case prior distribution. We describe the properties of the worst-case prior and present a recursive Bellman-type equation for determination of both minimax strategy and minimax risk. Numerical results illustrating the proposed algorithm are given. The algorithm can be applied to optimization of parallel data processing if the number of processed data is not definitely known in advance.
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