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Integral and inverse reinforcement learning for optimal control systems and games

Record Type:	Electronic resources : Monograph/item
Title/Author:	Integral and inverse reinforcement learning for optimal control systems and gamesby Bosen Lian ... [et al.].
other author:	Lian, Bosen.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xx, 267 p. :ill. (chiefly col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Reinforcement learning.
Online resource:	https://doi.org/10.1007/978-3-031-45252-9
ISBN:	9783031452529$q(electronic bk.)

Integral and inverse reinforcement learning for optimal control systems and games
Integral and inverse reinforcement learning for optimal control systems and games[electronic resource] /by Bosen Lian ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xx, 267 p. :ill. (chiefly col.), digital ;24 cm. - Advances in industrial control,2193-1577. - Advances in industrial control..

1. Introduction -- 2. Background on Integral and Inverse Reinforcement Learning for Dynamic System Feedback -- 3. Integral Reinforcement Learning for Optimal Regulation -- 4. Integral Reinforcement Learning for Optimal Tracking -- 5. Integral Reinforcement Learning for Nonlinear Tracker -- Integral Reinforcement Learning for H-infinity Control -- 6. Inverse Reinforcement Learning for Linear and Nonlinear Systems -- 7. Inverse Reinforcement Learning for Two-Player Zero-Sum Games -- 8. Inverse Reinforcement Learning for Multi-player Nonzero-sum Games.

Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games develops its specific learning techniques, motivated by application to autonomous driving and microgrid systems, with breadth and depth: integral reinforcement learning (RL) achieves model-free control without system estimation compared with system identification methods and their inevitable estimation errors; novel inverse RL methods fill a gap that will help them to attract readers interested in finding data-driven model-free solutions for inverse optimization and optimal control, imitation learning and autonomous driving among other areas. Graduate students will find that this book offers a thorough introduction to integral and inverse RL for feedback control related to optimal regulation and tracking, disturbance rejection, and multiplayer and multiagent systems. For researchers, it provides a combination of theoretical analysis, rigorous algorithms, and a wide-ranging selection of examples. The book equips practitioners working in various domains - aircraft, robotics, power systems, and communication networks among them - with theoretical insights valuable in tackling the real-world challenges they face.

ISBN: 9783031452529$q(electronic bk.)

Standard No.: 10.1007/978-3-031-45252-9doiSubjects--Topical Terms:

349131
Reinforcement learning.

LC Class. No.: Q325.6

Dewey Class. No.: 006.31

Integral and inverse reinforcement learning for optimal control systems and games
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245 0 0 $a Integral and inverse reinforcement learning for optimal control systems and games $h [electronic resource] / $c by Bosen Lian ... [et al.].
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505 0 $a 1. Introduction -- 2. Background on Integral and Inverse Reinforcement Learning for Dynamic System Feedback -- 3. Integral Reinforcement Learning for Optimal Regulation -- 4. Integral Reinforcement Learning for Optimal Tracking -- 5. Integral Reinforcement Learning for Nonlinear Tracker -- Integral Reinforcement Learning for H-infinity Control -- 6. Inverse Reinforcement Learning for Linear and Nonlinear Systems -- 7. Inverse Reinforcement Learning for Two-Player Zero-Sum Games -- 8. Inverse Reinforcement Learning for Multi-player Nonzero-sum Games.
520 $a Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games develops its specific learning techniques, motivated by application to autonomous driving and microgrid systems, with breadth and depth: integral reinforcement learning (RL) achieves model-free control without system estimation compared with system identification methods and their inevitable estimation errors; novel inverse RL methods fill a gap that will help them to attract readers interested in finding data-driven model-free solutions for inverse optimization and optimal control, imitation learning and autonomous driving among other areas. Graduate students will find that this book offers a thorough introduction to integral and inverse RL for feedback control related to optimal regulation and tracking, disturbance rejection, and multiplayer and multiagent systems. For researchers, it provides a combination of theoretical analysis, rigorous algorithms, and a wide-ranging selection of examples. The book equips practitioners working in various domains - aircraft, robotics, power systems, and communication networks among them - with theoretical insights valuable in tackling the real-world challenges they face.
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