Multi-Armed Bandits: Theory and Applications to Online Learning in Networks

Qing Zhao, Cornell University

Multi-armed bandit problems pertain to optimal sequential decision making and learning in unknown environments. Since the first bandit problem posed by Thompson in 1933 for the application of clinical trials, bandit problems have enjoyed lasting attention from multiple research communities and have found a wide range of applications across diverse domains. This book covers classic results and recent development on both Bayesian and frequentist bandit problems. 

 ISBN: 9781627056380 | PDF ISBN: 9781627058711
Hardcover ISBN: 9781681736372  

Morgan & Claypool Publishers 

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