{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/optimal-regret-analysis-of-thompson-sampling","title":"Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Plays","arxiv_id":"1506.00779","date":"2015-06-02","proceeding":null,"authors":["Junpei Komiyama","Junya Honda","Hiroshi Nakagawa"],"abstract":"We discuss a multiple-play multi-armed bandit (MAB) problem in which several\narms are selected at each round. Recently, Thompson sampling (TS), a randomized\nalgorithm with a Bayesian spirit, has attracted much attention for its\nempirically excellent performance, and it is revealed to have an optimal regret\nbound in the standard single-play MAB problem. In this paper, we propose the\nmultiple-play Thompson sampling (MP-TS) algorithm, an extension of TS to the\nmultiple-play MAB problem, and discuss its regret analysis. We prove that MP-TS\nfor binary rewards has the optimal regret upper bound that matches the regret\nlower bound provided by Anantharam et al. (1987). Therefore, MP-TS is the first\ncomputationally efficient algorithm with optimal regret. A set of computer\nsimulations was also conducted, which compared MP-TS with state-of-the-art\nalgorithms. We also propose a modification of MP-TS, which is shown to have\nbetter empirical performance.","url_abs":"http://arxiv.org/abs/1506.00779v3","url_pdf":"http://arxiv.org/pdf/1506.00779v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"optimal-regret-analysis-of-thompson-sampling","repo_url":"https://github.com/jkomiyama/multiplaybanditlib","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"thompson-sampling","task_name":"Thompson Sampling"}],"methods":[{"method_slug":"ts","method_name":"TS"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1506.00779","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}