English

Simple Algorithms for Dueling Bandits

Machine Learning 2019-06-19 v1 Machine Learning

Abstract

In this paper, we present simple algorithms for Dueling Bandits. We prove that the algorithms have regret bounds for time horizon T of order O(T^rho ) with 1/2 <= rho <= 3/4, which importantly do not depend on any preference gap between actions, Delta. Dueling Bandits is an important extension of the Multi-Armed Bandit problem, in which the algorithm must select two actions at a time and only receives binary feedback for the duel outcome. This is analogous to comparisons in which the rater can only provide yes/no or better/worse type responses. We compare our simple algorithms to the current state-of-the-art for Dueling Bandits, ISS and DTS, discussing complexity and regret upper bounds, and conducting experiments on synthetic data that demonstrate their regret performance, which in some cases exceeds state-of-the-art.

Keywords

Cite

@article{arxiv.1906.07611,
  title  = {Simple Algorithms for Dueling Bandits},
  author = {Tyler Lekang and Andrew Lamperski},
  journal= {arXiv preprint arXiv:1906.07611},
  year   = {2019}
}
R2 v1 2026-06-23T09:56:59.929Z