Description
Book SynopsisADP or Approximate Dynamic Programming has gone by many different names including: reinforcement learning (RL), adaptive critics (AC), and neuro-dynamic programming (NDP). The dynamic programming approach to decision and control problems involving nonlinear dynamic systems provides the optimal solution in any stochastic or uncertain environment.
Trade Review"…highly recommended to researchers, graduate students, engineers, and scientists…" (
E-STREAMS, February 2006)
"Clearly, this book is useful for researchers who do or want to do research on ADP." (IIE Transactions-Quality & Reliability Engineering, February 2006)
"…I would like to congratulate the editors, for putting together this wonderful collection of research contributions." (Computing Reviews.com, March 18, 2005)
Table of ContentsForeword.
1. ADP: goals, opportunities and principles.
Part I: Overview.
2. Reinforcement learning and its relationship to supervised learning.
3. Model-based adaptive critic designs.
4. Guidance in the use of adaptive critics for control.
5. Direct neural dynamic programming.
6. The linear programming approach to approximate dynamic programming.
7. Reinforcement learning in large, high-dimensional state spaces.
8. Hierarchical decision making.
Part II: Technical advances.
9. Improved temporal difference methods with linear function approximation.
10. Approximate dynamic programming for high-dimensional resource allocation problems.
11. Hierarchical approaches to concurrency, multiagency, and partial observability.
12. Learning and optimization - from a system theoretic perspective.
13. Robust reinforcement learning using integral-quadratic constraints.
14. Supervised actor-critic reinforcement learning.
15. BPTT and DAC - a common framework for comparison.
Part III: Applications.
16. Near-optimal control via reinforcement learning.
17. Multiobjective control problems by reinforcement learning.
18. Adaptive critic based neural network for control-constrained agile missile.
19. Applications of approximate dynamic programming in power systems control.
20. Robust reinforcement learning for heating, ventilation, and air conditioning control of buildings.
21. Helicopter flight control using direct neural dynamic programming.
22. Toward dynamic stochastic optimal power flow.
23. Control, optimization, security, and self-healing of benchmark power systems.
