Efficient Sampling Policy for Selecting a Subset With the Best

• 成果类型: Article
• 署名作者: Zhang, Gongbo; Chen, Bin; Jia, Qing-Shan; Peng, Yijie
• 署名单位: Peking University; National University of Defense Technology - China; Tsinghua University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3207871
• 发表日期: 2023
• 页码: 4904-4911
• 关键词: Bayesian; ranking and selection (R & S); Sequential sampling; stochastic control; subset selection

摘要

In this article, we study the problem of selecting a subset with the best of a finite number of alternatives under a fixed simulation budget. Our work aims to maximize the posterior probability of correctly selecting such a subset. We formulate the dynamic sampling decision as a stochastic control problem in a Bayesian setting. In an approximate dynamic programming paradigm, we propose a sequential sampling policy based on value function approximation. We analyze the asymptotic property of the proposed sampling policy. Numerical experiments demonstrate the efficiency of the proposed procedure.