This book investigates the application of Reinforcement Learning (RL) and Approximate Dynamic Programming (ADP) techniques to feedback control systems. It details the latest RL and ADP methods for decision-making and control in engineered systems, encompassing both single-player control and multi-player games, drawing on ideas from diverse fields to guide the control of systems like robots, industrial processes, and economic decision-making.

This work is presented as a significant contribution to the critical research fields of RL and ADP for modern complex systems. The book is positioned as providing timely guidance by pioneers in the field, integrating diverse ideas to address control challenges in various engineered systems. Its scope suggests a comprehensive resource for researchers and practitioners in control engineering and artificial intelligence.