Epsilon-Greedy Thompson Sampling to Bayesian Optimization

arXiv:2403.00540v1 Announce Type: new
Abstract: Thompson sampling (TS) serves as a solution for addressing the exploitation-exploration dilemma in Bayesian optimization (BO). While it prioritizes exploration by randomly generating and maximizing sample paths of Gaussian process (GP) posteriors, TS weakly manages its exploitation by gathering information about the true objective function after each exploration is performed. In this study, we incorporate the epsilon-greedy ($\varepsilon$-greedy) policy, a well-established selection strategy in reinforcement learning, into TS to improve its exploitation. We first delineate …

abstract arxiv bayesian cs.lg epsilon exploitation exploration function information math.oc optimization process sample sampling solution stat.ml true type