Adaptive and sequential experiment design is a well-studied area in numerous domains. We survey and synthesize the work of the online statistical learning paradigm referred to as multi-armed bandits integrating the existing research as a resource for a certain class of online experiments. We first explore the traditional stochastic model of a multi-armed bandit, then explore a taxonomic scheme of complications to that model, for each complication relating it to a specific requirement or consideration of the experiment design context. Finally, at the end of the paper, we present a table of known upper-bounds of regret for all studied algorithms providing both perspectives for future theoretical work and a decision-making tool for practitioners looking for theoretical guarantees.

本研究调查和综合了在线统计学习范例——称为多臂赌博机的领域，作为在线实验的某一类资源。我们首先探讨了传统的多臂赌博机的随机模型，然后探讨了复杂模型的分类模式，针对每种模型的复杂性与实验设计背景下的特定要求或考虑进行了说明。最后，我们提供了所有研究算法已知上限遗憾表格的决策工具，为未来理论工作提供了两方面的视角。

带随机多臂赌博机的在线实验设计调查