Optimal Best Arm Identification in Two-Armed Bandits with a Fixed Budget under a Small Gap
小均值差距下固定预算双臂老虎机的最优最佳臂识别
We consider fixed-budget best-arm identification in two-armed Gaussian bandit problems. One of the longstanding open questions is the existence of an optimal strategy under which the probability of misidentification matches a lower bound. We show that a strategy following the Neyman allocation rule (Neyman, 1934) is asymptotically optimal when the gap between the expected rewards is small. First, we review a lower bound derived by Kaufmann et al. (2016). Then, we propose the…