Bayesian Anytime m-top Exploration

Pieter Libin, Timothy Verstraeten, Diederik M. Roijers, Wenjia Wang, Kristof Theys, Ann Nowé

Abstract: We introduce Boundary Focused Thompson sampling (BFTS), a new Bayesian algorithm to solve the anytime m-top exploration problem, where the objective is to identify the m best arms in a multi-armed bandit. First, we consider a set of existing benchmark problems that consider sub-Gaussian reward distributions (i.e., Gaussian with fixed variance and categorical reward). Next, we introduce a new environment inspired by a real world decision problem concerning insect control for organic agriculture. This new environment encodes a Poisson rewards distribution. For all these benchmarks, we experimentally show that BFTS consistently outperforms AT-LUCB, the current state of the art algorithm.