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

Dimitrakakis, Christos; Ortner, Ronald

Softcover

ISBN 10: 3031108922 ISBN 13: 9783031108921

Publisher: Springer, 2023

Synopsis

Presents recent research in decision making under uncertainty, and in particular reinforcement learning and learning with expert advice

Relate the theory to practical problems in reinforcement learning, artificial intelligence and cognitive science

Gives a thorough understanding of statistical decision theory, the meaning of hypothesis testing, automatic methods for designing and interpreting experiments and the relation of statistical decision making to human decision making

About 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.  

Edition details

• Publisher: Springer
• Publication date: 2023
• Language: English
• ISBN 10: 3031108922
• ISBN 13: 9783031108921
• Binding: Paperback
• Edition number: 1
• Number of pages: 256