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We consider the correlated multiarmed bandit (MAB) problem in which therewards associated with each arm are modeled by a multivariate Gaussian randomvariable, and we investigate the influence of the assumptions in the Bayesianprior on the performance of the upper credible limit (UCL) algorithm and a newcorrelated UCL algorithm. We rigorously characterize the influence of accuracy,confidence, and correlation scale in the prior on the decision-makingperformance of the algorithms. Our results show how priors and correlationstructure can be leveraged to improve performance.

Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis