Handbook of Reinforcement Learning and Control

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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. 

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Mehr Informationen
ReiheStudies in Systems, Decision and Control
ISBN 9783030609924
Sprache Englisch
Ausgabe 1st ed. 2021
Erscheinungsdatum 25.06.2022
Umfang 833 Seiten
Genre Technik/Elektronik, Elektrotechnik, Nachrichtentechnik
Format Taschenbuch
Verlag Springer International Publishing
Herausgegeben von Kyriakos G. Vamvoudakis, Yan Wan, Frank L. Lewis, Derya Cansever
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