ISBN:
9783031452529
Language:
English
Pages:
1 Online-Ressource(XX, 267 p. 43 illus., 41 illus. in color.)
Edition:
1st ed. 2024.
Series Statement:
Advances in Industrial Control
Parallel Title:
Erscheint auch als
Parallel Title:
Erscheint auch als
Parallel Title:
Erscheint auch als
Keywords:
Control engineering.
;
Engineering mathematics.
;
Engineering
;
Computational intelligence.
;
Automotive engineering.
Abstract:
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.
Abstract:
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.
DOI:
10.1007/978-3-031-45252-9
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