标题： 在不完全信息和成本约束下的顺序采样自适应策略 显示英文标题

标题： Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint

摘要： 我们研究了从有限个独立统计总体中按顺序抽样的问题，目标是在期望平均抽样成本不超过上界约束下，最大化每期的期望无限 horizon 平均结果。分布是未知的。 我们构造了一类一致自适应策略，在这些策略下，对于均值有限的所有分布，平均结果以概率 1 收敛到完全信息下的真实值。 我们还使用模拟比较了此类策略中各种策略的收敛速度。 显示英文摘要

摘要： We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construct a class of consistent adaptive policies, under which the average outcome converges with probability 1 to the true value under complete information for all distributions with finite means. We also compare the rate of convergence for various policies in this class using simulation.