Optimal Thresholding Linear Bandit
arXiv:2402.09467
Abstract
We study a novel pure exploration problem: the -Thresholding Bandit Problem (TBP) with fixed confidence in stochastic linear bandits. We prove a lower bound for the sample complexity and extend an algorithm designed for Best Arm Identification in the linear case to TBP that is asymptotically optimal.
arXiv admin note: substantial text overlap with arXiv:2006.16073 by other authors