TY - BOOK ID - 119355659 TI - Reinforcement Learning : Optimal Feedback Control with Industrial Applications AU - Li, Jinna. AU - Lewis, Frank L. AU - Fan, Jialu. PY - 2023 SN - 3031283945 3031283937 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Control engineering. KW - Computational intelligence. KW - Production engineering. KW - Engineering mathematics. KW - Engineering—Data processing. KW - Industrial engineering. KW - System theory. KW - Control and Systems Theory. KW - Computational Intelligence. KW - Process Engineering. KW - Mathematical and Computational Engineering Applications. KW - Industrial and Production Engineering. KW - Complex Systems. KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing KW - Control engineering KW - Control equipment KW - Control theory KW - Engineering instruments KW - Automation KW - Programmable controllers KW - Management engineering KW - Simplification in industry KW - Engineering KW - Value analysis (Cost control) KW - Engineering analysis KW - Mathematical analysis KW - Manufacturing engineering KW - Process engineering KW - Industrial engineering KW - Mechanical engineering KW - Systems, Theory of KW - Systems science KW - Science KW - Mathematics KW - Philosophy UR - https://www.unicat.be/uniCat?func=search&query=sysid:119355659 AB - This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems. A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agent systems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed. The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries. ER -