Handbook of Reinforcement Learning and Control (Hardcover, 1st ed. 2021)

This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Systems, Decision and Control, 325
Release date:	June 2021
First published:	2021
Editors:	Kyriakos G. Vamvoudakis • Yan Wan • Frank L. Lewis • Derya Cansever
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	833
Edition:	1st ed. 2021
ISBN-13:	978-3-03-060989-4
Categories:	Books > Computing & IT > Social & legal aspects of computing > Privacy & data protection Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory Books > Professional & Technical > Energy technology & engineering > Electrical engineering > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	3-03-060989-8
Barcode:	9783030609894

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