Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis

Vaibhav Srivastava; Paul Reverdy; Naomi Ehrich Leonard

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arXiv · 1507.01160

Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis

Vaibhav Srivastava·Paul Reverdy·Naomi Ehrich Leonard

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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.

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Vaibhav Srivastava, Paul Reverdy, Naomi Ehrich Leonard. 2015-07-05. Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis. https://arxiv.org/abs/1507.01160

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