Introduction to Multi-Armed Bandits - (Foundations and Trends(r) in Machine Learning) by Aleksandrs Slivkins (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br>Multi-armed bandits is a rich, multi-disciplinary area that has been studied since 1933, with a surge of activity in the past 10-15 years. This is the first book to provide a textbook like treatment of the subject.<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>Multi-armed bandits is a rich, multi-disciplinary area that has been studied since 1933, with a surge of activity in the past 10-15 years. This is the first monograph to provide a textbook like treatment of the subject.</p><p>The work on multi-armed bandits can be partitioned into a dozen or so directions. Each chapter tackles one line of work, providing a self-contained introduction and pointers for further reading. <em>Introduction to Multi-Armed Bandits </em>concentrates on fundamental ideas and elementary, teachable proofs over the strongest possible results. It emphasizes accessibility of the material; while exposure to machine learning and probability/statistics would certainly help, a standard undergraduate course on algorithms should suffice for background.</p><p>The first four chapters are devoted IID rewards with adversarial rewards being covered in the next 3 chapters. Contextual bandits are discussed in a separate chapter before the monograph concludes with connections to economics. Each chapter contains a section on bibliographic notes and further directions. Many of the chapters conclude with some exercises.</p><p><em>Introduction to Multi-Armed Bandits </em>provides an accessible treatment for students of a topic that has gained importance in the last decade. Lecturers can use it as a text for an introductory course on the subject.</p>
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