https://arxiv.org/abs/1904.07272 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:1904.07272 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Machine Learning arXiv:1904.07272 (cs) [Submitted on 15 Apr 2019 (v1), last revised 3 Apr 2024 (this version, v8)] Title:Introduction to Multi-Armed Bandits Authors:Aleksandrs Slivkins View a PDF of the paper titled Introduction to Multi-Armed Bandits, by Aleksandrs Slivkins View PDF HTML (experimental) Abstract:Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject. Each chapter tackles a particular line of work, providing a self-contained, teachable technical introduction and a brief review of the further developments; many of the chapters conclude with exercises. The book is structured as follows. The first four chapters are on IID rewards, from the basic model to impossibility results to Bayesian priors to Lipschitz rewards. The next three chapters cover adversarial rewards, from the full-feedback version to adversarial bandits to extensions with linear rewards and combinatorially structured actions. Chapter 8 is on contextual bandits, a middle ground between IID and adversarial bandits in which the change in reward distributions is completely explained by observable contexts. The last three chapters cover connections to economics, from learning in repeated games to bandits with supply/budget constraints to exploration in the presence of incentives. The appendix provides sufficient background on concentration and KL-divergence. The chapters on "bandits with similarity information", "bandits with knapsacks" and "bandits and agents" can also be consumed as standalone surveys on the respective topics. Published with Foundations and Trends(R) in Machine Learning, November 2019. The present version is a revision Comments: of the "Foundations and Trends" publication. It contains numerous edits for presentation and accuracy (based in part on readers' feedback), updated and expanded literature reviews, and some new exercises Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Subjects: Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML) Cite as: arXiv:1904.07272 [cs.LG] (or arXiv:1904.07272v8 [cs.LG] for this version) https://doi.org/10.48550/arXiv.1904.07272 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Aleksandrs Slivkins [view email] [v1] Mon, 15 Apr 2019 18:17:01 UTC (510 KB) [v2] Mon, 29 Apr 2019 20:45:01 UTC (510 KB) [v3] Tue, 25 Jun 2019 14:39:03 UTC (536 KB) [v4] Sun, 15 Sep 2019 02:06:22 UTC (557 KB) [v5] Mon, 30 Sep 2019 00:15:42 UTC (543 KB) [v6] Sat, 26 Jun 2021 20:15:32 UTC (639 KB) [v7] Sat, 8 Jan 2022 20:05:40 UTC (627 KB) [v8] Wed, 3 Apr 2024 21:32:42 UTC (629 KB) Full-text links: Access Paper: View a PDF of the paper titled Introduction to Multi-Armed Bandits, by Aleksandrs Slivkins * View PDF * HTML (experimental) * TeX Source * Other Formats view license Current browse context: cs.LG < prev | next > new | recent | 2019-04 Change to browse by: cs cs.AI cs.DS stat stat.ML References & Citations * NASA ADS * Google Scholar * Semantic Scholar DBLP - CS Bibliography listing | bibtex Aleksandrs Slivkins a export BibTeX citation Loading... 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