04456nam a22006495i 4500 978-3-031-45252-9 DE-He213 20251030111501.0 cr nn 008mamaa 240305s2024 sz | s |||| 0|eng d 9783031452529 978-3-031-45252-9 10.1007/978-3-031-45252-9 doi TJ212-225 TJFM bicssc GPFC bicssc TEC007000 bisacsh TJFM thema Lian, Bosen. author. aut http://id.loc.gov/vocabulary/relators/aut 72423 Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games [electronic resource] / by Bosen Lian, Wenqian Xue, Frank L. Lewis, Hamidreza Modares, Bahare Kiumarsi. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XX, 267 p. 43 illus., 41 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Advances in Industrial Control, 2193-1577 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. Control engineering. Engineering mathematics. 64145 Engineering Data processing. Computational intelligence. 64052 Automotive engineering. Control and Systems Theory. 65868 Mathematical and Computational Engineering Applications. Computational Intelligence. 64052 Automotive Engineering. Xue, Wenqian. author. aut http://id.loc.gov/vocabulary/relators/aut 72424 Lewis, Frank L. author. aut http://id.loc.gov/vocabulary/relators/aut 72425 Modares, Hamidreza. author. aut http://id.loc.gov/vocabulary/relators/aut 72426 Kiumarsi, Bahare. author. aut http://id.loc.gov/vocabulary/relators/aut 72427 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031452512 Printed edition: 9783031452536 Printed edition: 9783031452543 Advances in Industrial Control, 2193-1577 14359 https://doi.org/10.1007/978-3-031-45252-9 ZDB-2-INR ZDB-2-SXIT 58109 58109