Libin, P., Verstraeten, T., Roijers, D. M., Wang, W., Theys, K., & Nowe, A. (2019). Bayesian Anytime m-top Exploration. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 1422-1428) https://doi.org/10.1109/ICTAI.2019.00201

@inproceedings{f2836ffe52204c32b17f50649f4f0964,
title = "Bayesian Anytime m-top Exploration",
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.",
author = "Pieter Libin and Timothy Verstraeten and Roijers, {Diederik M} and Wenjia Wang and Kristof Theys and Ann Nowe",
year = "2019",
month = nov,
doi = "10.1109/ICTAI.2019.00201",
language = "English",
isbn = "978-1-7281-3798-8",
pages = "1422--1428",
booktitle = "2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)",
note = "ICTAI ; Conference date: 04-11-2019 Through 06-11-2019",
}