Optimally Confident UCB: Improved Regret for Finite-Armed Bandits
Machine Learning
2016-02-25 v3 Optimization and Control
Abstract
I present the first algorithm for stochastic finite-armed bandits that simultaneously enjoys order-optimal problem-dependent regret and worst-case regret. Besides the theoretical results, the new algorithm is simple, efficient and empirically superb. The approach is based on UCB, but with a carefully chosen confidence parameter that optimally balances the risk of failing confidence intervals against the cost of excessive optimism.
Keywords
Cite
@article{arxiv.1507.07880,
title = {Optimally Confident UCB: Improved Regret for Finite-Armed Bandits},
author = {Tor Lattimore},
journal= {arXiv preprint arXiv:1507.07880},
year = {2016}
}
Comments
26 pages