Integral and Inverse Reinforcement Learning for Optimal Control Systems and Games
No. Klas
:
-
Pengarang
:
Bosen Lian, Wenqian Xue, Frank L. Lewis, Hamidreza Modares, Bahare Kiumarsi
Penerbit
:
Springer, -, 2024
Kolasi
:
-
Digital Copy
:
1
Pinjaman Aktif
:
0
Synopsis
:
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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