@inproceedings{e9173bacfde346c8bf9e2cc14e6b1bff,
title = "Improving Particle Thompson Sampling through Regenerative Particles",
abstract = "This paper proposes regenerative particle Thompson sampling (RPTS) as an improvement of particle Thompson sampling (PTS) for solving general stochastic bandit problems. PTS approximates Thompson sampling by replacing the continuous posterior distribution with a discrete distribution supported at a set of weighted static particles. PTS is flexible but may suffer from poor performance due to the tendency of the probability mass to concentrate on a small number of particles. RPTS exploits the particle weight dynamics of PTS and uses non-static particles: it deletes a particle if its probability mass gets sufficiently small and regenerates new particles in the vicinity of the surviving particles. Empirical evidence shows uniform improvement across a set of representative bandit problems without increasing the number of particles.",
keywords = "Thompson sampling, particles, stochastic bandit",
author = "Zeyu Zhou and Bruce Hajek",
note = "This work was supported in part by NSF Grant Grant CCF 19-00636. It was done while the first author was a Ph.D. student at the Electrical and Computer Engineering Department of UIUC This work was supported in part by NSF Grant Grant CCF 19-00636. It was done while the first author was a Ph.D. student at the Electrical and Computer Engineering Department of UIUC.; 57th Annual Conference on Information Sciences and Systems, CISS 2023 ; Conference date: 22-03-2023 Through 24-03-2023",
year = "2023",
doi = "10.1109/CISS56502.2023.10089647",
language = "English (US)",
series = "2023 57th Annual Conference on Information Sciences and Systems, CISS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 57th Annual Conference on Information Sciences and Systems, CISS 2023",
address = "United States",
}