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001472171 019__ $$a1391129625
001472171 020__ $$a9783031283949
001472171 020__ $$a3031283945
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001472171 020__ $$z9783031283932
001472171 0247_ $$a10.1007/978-3-031-28394-9$$2doi
001472171 035__ $$aSP(OCoLC)1391443173
001472171 040__ $$aEBLCP$$beng$$cEBLCP$$dYDX$$dGW5XE$$dEBLCP
001472171 049__ $$aISEA
001472171 050_4 $$aQ325.6
001472171 08204 $$a006.3/1$$223/eng/20230731
001472171 1001_ $$aLi, Jinna.
001472171 24510 $$aReinforcement Learning :$$bOptimal Feedback Control with Industrial Applications /$$cJinna Li, Frank L. Lewis, Jialu Fan.
001472171 260__ $$aCham :$$bSpringer International Publishing AG,$$c2023.
001472171 300__ $$a1 online resource (xvi, 310 pages) :$$billustrations (chiefly color).
001472171 4901_ $$aAdvances in Industrial Control
001472171 504__ $$aIncludes bibliographical references and index.
001472171 5050_ $$aIntro -- Series Editor's Foreword -- Preface -- Acknowledgements -- Contents -- Abbreviations -- 1 Background on Reinforcement Learning and Optimal Control -- 1.1 Fundamentals of Reinforcement Learning and Recall -- 1.2 Fundamentals of Optimal Control with Dynamic Programming -- 1.3 Architecture and Performance of Networked Control System -- 1.4 The State of the Art and Contributions -- References -- 2 Hinfty Control Using Reinforcement Learning -- 2.1 Hinfty State Feedback Control of Multi-player Systems -- 2.1.1 Problem Statement -- 2.1.2 Solving the Multi-player Zero-Sum Game
001472171 5058_ $$a2.1.3 Off-Policy Game Q-Learning Technique -- 2.1.4 Simulation Results -- 2.2 Hinfty Output Feedback Control of Multi-player Systems -- 2.2.1 Problem Statement -- 2.2.2 Solving the Multi-player Zero-Sum Game -- 2.2.3 Off-Policy Game Q-Learning Technique -- 2.2.4 Simulation Results -- 2.3 Conclusion -- References -- 3 Robust Tracking Control and Output Regulation -- 3.1 Optimal Robust Control Problem Statement -- 3.2 Theoretical Solutions -- 3.2.1 Solving the Regulator Equations with Known Dynamics -- 3.2.2 Solving Problem 3.2 with Known Dynamics -- 3.3 Data-Driven Solutions
001472171 5058_ $$a3.3.1 Data-Driven OPCG Q-Learning -- 3.3.2 No Bias Analysis of the Solution for the Proposed Algorithm -- 3.4 Illustrative Examples -- 3.5 Conclusion -- References -- 4 Interleaved Robust Reinforcement Learning -- 4.1 Robust Controller Design and Simplified HJB Equation -- 4.2 Interleaved RL for Approximating Robust Control -- 4.2.1 Theoretical Analysis -- 4.3 Illustrative Examples -- 4.4 Conclusion -- References -- 5 Optimal Networked Controller and Observer Design -- 5.1 Off-Policy Q-Learning for Single-Player Networked Control Systems -- 5.1.1 Problem Formulation
001472171 5058_ $$a5.1.2 Optimal Observer Design -- 5.1.3 Optimal Controller Design -- 5.1.4 Simulation Results -- 5.2 Off-Policy Q-Learning for Multi-player Networked Control Systems -- 5.2.1 Problem Formulation -- 5.2.2 Main Results -- 5.2.3 Illustrative Example -- 5.3 Conclusion -- References -- 6 Interleaved Q-Learning -- 6.1 Optimal Control for Affine Nonlinear Systems -- 6.1.1 Problem Statement -- 6.1.2 On-Policy Q-Learning Formulation -- 6.2 Off-Policy Q-Learning Technique -- 6.2.1 Off-Policy and Q-Learning -- 6.2.2 Derivation of Off-Policy Q-Learning Algorithm
001472171 5058_ $$a6.2.3 No Bias of Off-Policy Q-Learning Algorithm -- 6.3 Neural Network-Based Off-Policy Interleaved Q-Learning -- 6.3.1 Model Neural Network -- 6.3.2 Actor Neural Network -- 6.3.3 Critic Neural Network -- 6.3.4 Interleaved Q-Learning -- 6.3.5 Optimal Control for Linear Systems -- 6.4 Illustrative Examples -- 6.5 Conclusion -- References -- 7 Off-Policy Game Reinforcement Learning -- 7.1 Graphical Game for Optimal Synchronization -- 7.1.1 Preliminaries -- 7.1.2 Multi-agent Graphical Games -- 7.1.3 Off-Policy Reinforcement Learning Algorithm -- 7.1.4 Simulation Examples
001472171 506__ $$aAccess limited to authorized users.
001472171 520__ $$aThis book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems. A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agent systems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed. The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries.
001472171 588__ $$aDescription based upon print version of record.
001472171 650_0 $$aReinforcement learning.
001472171 650_0 $$aFeedback control systems.
001472171 655_0 $$aElectronic books.
001472171 7001_ $$aLewis, Frank L.
001472171 7001_ $$aFan, Jialu.
001472171 77608 $$iPrint version:$$aLi, Jinna$$tReinforcement Learning$$dCham : Springer International Publishing AG,c2023$$z9783031283932
001472171 830_0 $$aAdvances in industrial control.
001472171 852__ $$bebk
001472171 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-28394-9$$zOnline Access$$91397441.1
001472171 909CO $$ooai:library.usi.edu:1472171$$pGLOBAL_SET
001472171 980__ $$aBIB
001472171 980__ $$aEBOOK
001472171 982__ $$aEbook
001472171 983__ $$aOnline
001472171 994__ $$a92$$bISE