Fixed Budget Best Arm Identification

Fixed-budget best arm identification (BAI) focuses on efficiently selecting the most rewarding option from a set of possibilities within a predetermined number of trials. Current research emphasizes developing algorithms that leverage prior information, adapt to structured environments (like linear bandits or hierarchical models), and exhibit robustness to non-stationarity, often employing techniques like upper confidence bounds (UCBs), successive rejection, or inverse probability weighting. These advancements aim to improve the accuracy and efficiency of decision-making in various applications, such as A/B testing and online model selection, by providing theoretically sound and practically effective methods for identifying the optimal arm.