  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.
