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Description
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.
Table of Contents
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.
