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• W2069005539 abstract "The multiarmed-bandit problem is often taken as a basic model for the trade-off between the exploration and utilization required for efficient optimization under uncertainty. In this article, we study the situation in which the unknown performance of a new bandit is to be evaluated and compared with that of a known one over a finite horizon. We assume that the bandits represent random variables with distributions from the one-parameter exponential family. When the objective is to maximize the Bayes expected sum of outcomes over a finite horizon, it is shown that optimal policies tend to simple limits when the length of the horizon is large." @default.
• W2069005539 created "2016-06-24" @default.
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• W2069005539 date "2003-01-01" @default.
• W2069005539 modified "2023-10-02" @default.
• W2069005539 title "ASYMPTOTIC BAYES ANALYSIS FOR THE FINITE-HORIZON ONE-ARMED-BANDIT PROBLEM" @default.
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• W2069005539 doi "https://doi.org/10.1017/s0269964803171045" @default.
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