Decision Making Under Uncertainty and Reinforcement Learning - Theory and Algorithms (Hardcover, 1st ed. 2022)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Intelligent Systems Reference Library, 223
Release date:	December 2022
First published:	2022
Authors:	Christos Dimitrakakis • Ronald Ortner
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	243
Edition:	1st ed. 2022
ISBN-13:	978-3-03-107612-1
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-107612-5
Barcode:	9783031076121

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