Decision Making Under Uncertainty and Reinforcement Learning

Lieferzeit: Lieferbar innerhalb 14 Tagen

171,19 

Theory and Algorithms, Intelligent Systems Reference Library 223

ISBN: 3031108922
ISBN 13: 9783031108921
Autor: Dimitrakakis, Christos/Ortner, Ronald
Verlag: Springer Verlag GmbH
Umfang: xiii, 243 S., 5 s/w Illustr., 62 farbige Illustr., 243 p. 67 illus., 62 illus. in color.
Erscheinungsdatum: 07.12.2023
Auflage: 1/2023
Produktform: Kartoniert
Einband: Kartoniert

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.

Artikelnummer: 5940532 Kategorie:

Beschreibung

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.  

Herstellerkennzeichnung:


Springer Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

E-Mail: juergen.hartmann@springer.com

Das könnte Ihnen auch gefallen …