Аннотация
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. The chapters are as follows: stochastic bandits, lower bounds;
Bayesian bandits and Thompson Sampling; Lipschitz Bandits; full Feedback and
adversarial costs; adversarial bandits; linear costs and semi-bandits;
contextual Bandits; bandits and games; bandits with knapsacks; bandits and
incentives.
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