论文标题
Neyman的分配是最佳的最佳选择，可与两个臂一起使用
Neyman allocation is minimax optimal for best arm identification with two arms
论文作者
论文摘要
根据本地的渐近minimax遗憾标准，本说明描述了最佳政策规则，以便在只有两种治疗方法时最佳手臂识别。结果表明，最佳抽样规则是Neyman分配，该分配以与治疗结果的标准偏差成正比的方式分配给每个处理的恒定分数。当方差相等时，最佳比率为一半。该策略独立于数据，因此对以前的结果没有适应性。在实验结束时，决策者采用了更高平均结果的治疗方法。
This note describes the optimal policy rule, according to the local asymptotic minimax regret criterion, for best arm identification when there are only two treatments. It is shown that the optimal sampling rule is the Neyman allocation, which allocates a constant fraction of units to each treatment in a manner that is proportional to the standard deviation of the treatment outcomes. When the variances are equal, the optimal ratio is one-half. This policy is independent of the data, so there is no adaptation to previous outcomes. At the end of the experiment, the policy maker adopts the treatment with higher average outcomes.
