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.

Original languageEnglish
Title of host publication2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)
Number of pages7
ISBN (Electronic)2375-0197
Publication statusPublished - Nov 2019
EventICTAI - Portland, United States
Duration: 4 Nov 20196 Nov 2019


CountryUnited States

ID: 49372548