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Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis

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.

preprint2015arXivOpen access
Vaibhav SrivastavaPaul ReverdyNaomi Ehrich Leonard
math.OCMachine Learning
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Vaibhav SrivastavaPaul ReverdyNaomi Ehrich Leonard

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