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Bayesian Anytime m-top Exploration

Boekbijdrage - Boekhoofdstuk Conferentiebijdrage

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.

Boek: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)
Pagina's: 1422-1428
Aantal pagina's: 7
Jaar van publicatie:2019
  • ORCID: /0000-0001-6346-4564/work/69210933
  • Scopus Id: 85081086058