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.
Cite
CITATION STYLE
APA
Slivkins, A. (2019). Introduction to multi-armed bandits. Foundations and Trends in Machine Learning, 12(1–2), 1–286. https://doi.org/10.1561/2200000068
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