Decision Making Under Uncertainty and Reinforcement Learning: Theory and Algorithms: 223 (Intelligent Systems Reference Library, 223) - Hardcover

Dimitrakakis, Christos; Ortner, Ronald

 
9783031076121: Decision Making Under Uncertainty and Reinforcement Learning: Theory and Algorithms: 223 (Intelligent Systems Reference Library, 223)

Synopsis

This book presents recent research in decision making under uncertainty, in particular reinforcement learning and learning with expert advice. The core elements of decision theory, Markov decision processes and reinforcement learning have not been previously collected in a concise volume. Our aim with this book was to provide a solid theoretical foundation with elementary proofs of the most important theorems in the field, all collected in one place, and not typically found in
introductory textbooks.  This book is addressed to graduate students that are interested in statistical decision making under uncertainty and the foundations of reinforcement learning.  


"synopsis" may belong to another edition of this title.

From the Back Cover

This book presents recent research in decision making under uncertainty, in particular reinforcement learning and learning with expert advice. The core elements of decision theory, Markov decision processes and reinforcement learning have not been previously collected in a concise volume. Our aim with this book was to provide a solid theoretical foundation with elementary proofs of the most important theorems in the field, all collected in one place, and not typically found in
introductory textbooks.  This book is addressed to graduate students that are interested in statistical decision making under uncertainty and the foundations of reinforcement learning.  

"About this title" may belong to another edition of this title.

Other Popular Editions of the Same Title

9783031108921: Decision Making Under Uncertainty and Reinforcement Learning: Theory and Algorithms: 223 (Intelligent Systems Reference Library, 223)

Featured Edition

ISBN 10:  3031108922 ISBN 13:  9783031108921
Publisher: Springer, 2023
Softcover