β Scribed by Frank L. Lewis, Derong Liu
No coin nor oath required. For personal study only.
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 together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.
Title page......Page 1
Contents......Page 5
Preface......Page 18
1. Reinforcement Learning and Approximate Dynamic Programming (RLADP)-Foundations, Common Misconceptions, and the Challenges Ahead......Page 26
2. Stable Adaptive Neural Control of Partially Observable Dynamic Systems......Page 54
3. Optimal Control of Unknown Nonlinear Discrete-Time Systems Using the Iterative Globalized Dual Heuristic Programming Algorithm......Page 75
4. Learning and Optimization in Hierarchical Adaptive Critic Design......Page 101
5. Single Network Adaptive Critics Networks-Development, Analysis, and Applications......Page 121
6. Linearly Solvable Optimal Control......Page 142
7. Approximating Optimal Control withValue Gradient Learning......Page 165
8. A Constrained Backpropagation Approach to Function Approximation and Approximate Dynamic Programming......Page 185
9. Toward Design of Nonlinear ADP Learning Controllers with Performance Assurance......Page 205
10. Reinforcement Learning Control with Time-Dependent Agent Dynamics......Page 226
11. Online Optimal Control of Nonaffine Nonlinear Discrete-Time Systems without Using Value and Policy Iterations......Page 244
12. An Actor-Critic-Identifier Architecture for Adaptive Approximate Optimal Control......Page 281
13. Robust Adaptive Dynamic Programming......Page 304
14. Hybrid Learning in Stochastic Games and Its Application in Network Security......Page 327
15. Integral Reinforcement Learning for Online Computation of Nash Strategies of Nonzero-Sum Differential Games......Page 352
16. Online Learning Algorithms for Optimal Control and Dynamic Games......Page 372
17. Lambda-Policy Iteration: A Review and a New Implementation......Page 401
18. Optimal Learning and Approximate Dynamic Programming......Page 430
19. An Introduction to Event-Based Optimization: Theory and Applications......Page 452
20. Bounds for Markov Decision Processes......Page 472
21. Approximate Dynamic Programming and Backpropagation on Timescales......Page 494
22. A Survey of Optimistic Planning in Markov Decision Processes......Page 514
23. Adaptive Feature Pursuit: Online Adaptation of Features in Reinforcement Learning......Page 537
24. Feature Selection for Neuro-Dynamic Programming......Page 555
25. Approximate Dynamic Programming for Optimizing Oil Production......Page 580
26. A Learning Strategy for Source Tracking in Unstructured Environments......Page 602
Index......Page 621
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