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Reinforcement learning = optimal fee...

Li, Jinna.

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Reinforcement learning = optimal feedback control with industrial applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Reinforcement learning/ by Jinna Li, Frank L. Lewis, Jialu Fan.
Reminder of title:	optimal feedback control with industrial applications /
Author:	Li, Jinna.
other author:	Lewis, Frank L.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xvi, 310 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	1. Background on Reinforcement Learning and Optimal Control -- 2. H-infinity Control Using Reinforcement Learning -- 3. Robust Tracking Control and Output Regulation -- 4. Interleaved Robust Reinforcement Learning -- 5. Optimal Networked Controller and Observer Design -- 6. Interleaved Q-Learning -- 7. Off-Policy Game Reinforcement Learning -- 8. Game Reinforcement Learning for Process Industries.
Contained By:	Springer Nature eBook
Subject:	Reinforcement learning. -
Online resource:	https://doi.org/10.1007/978-3-031-28394-9
ISBN:	9783031283949

Reinforcement learning = optimal feedback control with industrial applications /
Li, Jinna.

Reinforcement learningoptimal feedback control with industrial applications /[electronic resource] :by Jinna Li, Frank L. Lewis, Jialu Fan. - Cham :Springer International Publishing :2023. - xvi, 310 p. :ill. (chiefly color), digital ;24 cm. - Advances in industrial control,2193-1577. - Advances in industrial control..

1. Background on Reinforcement Learning and Optimal Control -- 2. H-infinity Control Using Reinforcement Learning -- 3. Robust Tracking Control and Output Regulation -- 4. Interleaved Robust Reinforcement Learning -- 5. Optimal Networked Controller and Observer Design -- 6. Interleaved Q-Learning -- 7. Off-Policy Game Reinforcement Learning -- 8. Game Reinforcement Learning for Process Industries.

This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems. A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agent systems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed. The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries.

ISBN: 9783031283949

Standard No.: 10.1007/978-3-031-28394-9doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6

Dewey Class. No.: 006.31

Reinforcement learning = optimal feedback control with industrial applications /
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245 1 0 $a Reinforcement learning $h [electronic resource] : $b optimal feedback control with industrial applications / $c by Jinna Li, Frank L. Lewis, Jialu Fan.
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300 $a xvi, 310 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Advances in industrial control, $x 2193-1577
505 0 $a 1. Background on Reinforcement Learning and Optimal Control -- 2. H-infinity Control Using Reinforcement Learning -- 3. Robust Tracking Control and Output Regulation -- 4. Interleaved Robust Reinforcement Learning -- 5. Optimal Networked Controller and Observer Design -- 6. Interleaved Q-Learning -- 7. Off-Policy Game Reinforcement Learning -- 8. Game Reinforcement Learning for Process Industries.
520 $a This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems. A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agent systems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed. The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries.
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650 0 $a Feedback control systems. $3 531860
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650 2 4 $a Computational Intelligence. $3 1001631
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650 2 4 $a Mathematical and Computational Engineering Applications. $3 3592737
650 2 4 $a Industrial and Production Engineering. $3 891024
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700 1 $a Lewis, Frank L. $3 667303
700 1 $a Fan, Jialu. $3 1569058
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830 0 $a Advances in industrial control. $3 1566025
856 4 0 $u https://doi.org/10.1007/978-3-031-28394-9
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)