Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis
Optimization and Control
2015-07-09 v2 Machine Learning
Machine Learning
Abstract
We consider the correlated multiarmed bandit (MAB) problem in which the rewards associated with each arm are modeled by a multivariate Gaussian random variable, and we investigate the influence of the assumptions in the Bayesian prior on the performance of the upper credible limit (UCL) algorithm and a new correlated UCL algorithm. We rigorously characterize the influence of accuracy, confidence, and correlation scale in the prior on the decision-making performance of the algorithms. Our results show how priors and correlation structure can be leveraged to improve performance.
Cite
@article{arxiv.1507.01160,
title = {Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis},
author = {Vaibhav Srivastava and Paul Reverdy and Naomi Ehrich Leonard},
journal= {arXiv preprint arXiv:1507.01160},
year = {2015}
}