This paper targets a variant of the stochastic multi-armed bandit problem called good arm identification (GAI). GAI is a pure-exploration bandit problem with the goal to output as many good arms using as few samples as possible, where a good arm is defined as an arm whose expected reward is greater than a given threshold. In this work, we propose DGAI - a differentiable good arm identification algorithm to improve the sample complexity of the state-of-the-art HDoC algorithm in a data-driven fashion. We also showed that the DGAI can further boost the performance of a general multi-arm bandit (MAB) problem given a threshold as a prior knowledge to the arm set. Extensive experiments confirm that our algorithm outperform the baseline algorithms significantly in both synthetic and real world datasets for both GAI and MAB tasks.

翻译：本文针对随机多臂老虎机问题的一个变体——好臂识别（GAI）展开研究。GAI是一种纯探索型老虎机问题，目标是在尽可能少的样本下输出尽可能多的好臂，其中好臂定义为期望奖励大于给定阈值的臂。在本工作中，我们提出了DGAI——一种可微的好臂识别算法，以数据驱动的方式提升现有最优算法HDoC的样本复杂度。我们还证明，当以阈值为臂集合的先验知识时，DGAI能够进一步优化一般多臂老虎机（MAB）问题的性能。大量实验表明，在合成数据集和真实世界数据集上，我们的算法在GAI和MAB任务中均显著优于基线算法。