Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games / / by Bosen Lian, Wenqian Xue, Frank L. Lewis, Hamidreza Modares, Bahare Kiumarsi

• Autore: Lian Bosen
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (278 pages)
• Disciplina: 006.31
• Soggetto topico: Control engineering; Engineering mathematics; Engineering - Data processing; Computational intelligence; Automotive engineering; Control and Systems Theory; Mathematical and Computational Engineering Applications; Computational Intelligence; Automotive Engineering
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games
• ISBN: 3-031-45252-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910842291503321
• Lo trovi qui: Univ. Federico II
• Serie: Advances in Industrial Control, . 2193-1577