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Reinforcement learning and approximate dynamic programming for feedback control

Author: Frank L Lewis; Derong Liu
Publisher: Hoboken, New Jersey : IEEE Press, [2012]
Series: IEEE series on computational intelligence.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Reinforcement learning and approximate dynamic programming for feedback control.
Malden, MA : Wiley, 2013
(DLC) 2012019014
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Frank L Lewis; Derong Liu
ISBN: 9781118453971 1118453972 9781118453933 111845393X 9781118453964 1118453964 9781118453988 1118453980
OCLC Number: 798809964
Description: 1 online resource.
Contents: Series Page; Title Page; Copyright; Preface; Contributors; Part I: Feedback Control Using RL And ADP; Chapter 1: Reinforcement Learning and Approximate Dynamic Programming (RLADP)-Foundations, Common Misconceptions, and the Challenges Ahead; 1.1 Introduction; 1.2 What is RLADP?; 1.3 Some Basic Challenges in Implementing ADP; Disclaimer; References; Chapter 2: Stable Adaptive Neural Control of Partially Observable Dynamic Systems; 2.1 Introduction; 2.2 Background; 2.3 Stability Bias; 2.4 Example Application; References. Chapter 3: Optimal Control of Unknown Nonlinear Discrete-Time Systems Using the Iterative Globalized Dual Heuristic Programming Algorithm3.1 Background Material; 3.2 Neuro-Optimal Control Scheme Based on the Iterative ADP Algorithm; 3.3 Generalization; 3.4 Simulation Studies; 3.5 Summary; References; Chapter 4: Learning and Optimization in Hierarchical Adaptive Critic Design; 4.1 Introduction; 4.2 Hierarchical ADP Architecture with Multiple-Goal Representation; 4.3 Case Study: The Ball-and-Beam System; 4.4 Conclusions and Future Work; Acknowledgments; References. Chapter 5: Single Network Adaptive Critics Networks-Development, Analysis, and Applications5.1 Introduction; 5.2 Approximate Dynamic Programing; 5.3 SNAC; 5.4 J-SNAC; 5.5 Finite-SNAC; 5.6 Conclusions; Acknowledgments; References; Chapter 6: Linearly Solvable Optimal Control; 6.1 Introduction; 6.2 Linearly Solvable Optimal Control Problems; 6.3 Extension to Risk-Sensitive Control and Game Theory; 6.4 Properties and Algorithms; 6.5 Conclusions and Future Work; References; Chapter 7: Approximating Optimal Control with Value Gradient Learning; 7.1 Introduction. 7.2 Value Gradient Learning and BPTT Algorithms7.3 A Convergence Proof for VGL(1) for Control with Function Approximation; 7.4 Vertical Lander Experiment; 7.5 Conclusions; References; Chapter 8: A Constrained Backpropagation Approach to Function Approximation and Approximate Dynamic Programming; 8.1 Background; 8.2 Constrained Backpropagation (CPROP) Approach; 8.3 Solution of Partial Differential Equations in Nonstationary Environments; 8.4 Preserving Prior Knowledge in Exploratory Adaptive Critic Designs; 8.5 Summary; Algebraic ANN Control Matrices; References. Chapter 9: Toward Design of Nonlinear ADP Learning Controllers with Performance Assurance9.1 Introduction; 9.2 Direct Heuristic Dynamic Programming; 9.3 A Control Theoretic View on the Direct HDP; 9.4 Direct HDP Design with Improved Performance Case 1-Design Guided by a Priori LQR Information; 9.5 Direct HDP Design with Improved Performance Case 2-Direct HDP for Coorindated Damping Control of Low-Frequency Oscillation; 9.6 Summary; Acknowledgment; References; Chapter 10: Reinforcement Learning Control with Time-Dependent Agent Dynamics; 10.1 Introduction; 10.2 Q-Learning.
Series Title: IEEE series on computational intelligence.
Responsibility: edited by Frank L. Lewis, UTA Automation and Robotics Research Institute, Fort Worth, TX Derong Liu, University of Illinois, Chicago, IL.

Abstract:

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems.  Read more...

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