Thresholding Bandit Problem with Both Duels and Pulls
Abstract
The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean rewards greater than a given threshold. We consider a new setting of TBP, where in addition to pulling arms, one can also \emph{duel} two arms and get the arm with a greater mean. In our motivating application from crowdsourcing, dueling two arms can be more cost-effective and time-efficient than direct pulls. We refer to this problem as TBP with Dueling Choices (TBP-DC). This paper provides an algorithm called Rank-Search (RS) for solving TBP-DC by alternating between ranking and binary search. We prove theoretical guarantees for RS, and also give lower bounds to show the optimality of it. Experiments show that RS outperforms previous baseline algorithms that only use pulls or duels.
Keywords
Cite
@article{arxiv.1910.06368,
title = {Thresholding Bandit Problem with Both Duels and Pulls},
author = {Yichong Xu and Xi Chen and Aarti Singh and Artur Dubrawski},
journal= {arXiv preprint arXiv:1910.06368},
year = {2020}
}
Comments
15 pages, 8 figures; The 23rd International Conference on Artificial Intelligence and Statistics