Handbook of Reinforcement Learning and Control
Vamvoudakis, G. K.
Handbook of Reinforcement Learning and Control [Electronic Resource] / edited by G. K. Vamvoudakis and others. - Cham : Springer International Publishing, 2021 - 839p. - Studies in Systems, Decision and Control Series Vol. 325 .
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.
9783030609900
Automatic Control-Sensitivity
Reinforcement Learning
629.836 / V259H
Handbook of Reinforcement Learning and Control [Electronic Resource] / edited by G. K. Vamvoudakis and others. - Cham : Springer International Publishing, 2021 - 839p. - Studies in Systems, Decision and Control Series Vol. 325 .
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.
9783030609900
Automatic Control-Sensitivity
Reinforcement Learning
629.836 / V259H