Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis

Autor:	Srivastava, Vaibhav, Reverdy, Paul, Leonard, Naomi Ehrich
Rok vydání:	2015
Předmět:	Mathematics - Optimization and Control Computer Science - Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1507.01160 Zobrazit plný text záznamu View this record from Arxiv