TY  - BOOK
AU  - Li,Jinna
AU  - Lewis,Frank L.
AU  - Fan,Jialu
ED  - SpringerLink (Online service)
TI  - Reinforcement Learning: Optimal Feedback Control with Industrial Applications 
T2  - Advances in Industrial Control,
SN  - 9783031283949
AV  - TJ212-225 
U1  - 629.8312 23
PY  - 2023///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Control engineering
KW  - Computational intelligence
KW  - Production engineering
KW  - Engineering mathematics
KW  - Engineering
KW  - 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
N1  - Acceso multiusuario; 1. Background on Reinforcement Learning and Optimal Control -- 2. H-infinity Control Using Reinforcement Learning -- 3. Robust Tracking Control and Output Regulation -- 4. Interleaved Robust Reinforcement Learning -- 5. Optimal Networked Controller and Observer Design -- 6. Interleaved Q-Learning -- 7. Off-Policy Game Reinforcement Learning -- 8. Game Reinforcement Learning for Process Industries
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-28394-9
ER  -