Lian / Xue / Lewis

Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games

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149,79 €

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auch verfügbar als eBook (PDF) für 139,09 €

Fachbuch

Buch. Hardcover

2024

xx, 267 S. 2 s/w-Abbildungen, 41 Farbabbildungen.

In englischer Sprache

Springer. ISBN 978-3-031-45251-2

Format (B x L): 15,5 x 23,5 cm

Produktbeschreibung

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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