Integral and inverse reinforcement learning for optimal control systems and 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.