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Image of - Handbook Of Reinforcement Learning And Control | Hardcover
Handbook Of Reinforcement Learning And Control | Hardcover

Handbook Of Reinforcement Learning And Control | Hardcover

by Vamvoudakis K.G.

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; andmulti-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. 

Highlights

  • binding-icon

    9783030609894

    ISBN:

  • binding-icon

    Vamvoudakis K.G.

    Author:

  • binding-icon

    833

    Pages:

  • binding-icon

    1450 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2021

    Year:

  • edition-icon

    1st Edition

    Edition:

  • binding-icon

    Hardcover

    Binding:

₹18592

₹23240

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; andmulti-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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