TY  - BOOK
AU  - Lian,Bosen
AU  - Xue,Wenqian
AU  - Lewis,Frank L.
AU  - Modares,Hamidreza
AU  - Kiumarsi,Bahare
ED  - SpringerLink (Online service)
TI  - Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games
T2  - Advances in Industrial Control,
SN  - 9783031452529
AV  - TJ212-225 
U1  - 629.8312 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Control engineering
KW  - Engineering mathematics
KW  - Engineering
KW  - Data processing
KW  - Computational intelligence
KW  - Automotive engineering
KW  - Control and Systems Theory
KW  - Mathematical and Computational Engineering Applications
KW  - Computational Intelligence
KW  - Automotive Engineering
N1  - 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-45252-9
ER  -