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Title: Handbook of learning and approximate dynamic programming
Author (s): Barto, Andrew G.
Powell, Warren Buckler
Si, Jennie
Wunsch, Donald C.
Department/Lab Affiliations: Applied Computational Intelligence Laboratory
Electrical and Computer Engineering
Intelligent Systems Center
Table of Contents: 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.
Subject Terms: Automatic programming (Computer science).
Control theory.
Dynamic programming.
Machine learning.
Systems engineering.
Issue Date: 2004
Publisher: Wiley-Interscience
Citation: Jennie Si, Andrew G. Barto, Warren Buckler Powell, and Donald C. Wunsch II. Handbook of Learning and Approximate Dynamic Programming. Piscataway, NJ : Wiley-Interscience, 2004
Type: Book
text
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titleHandbook of learning and approximate dynamic programming
contributor.authorBarto, Andrew G.
contributor.authorPowell, Warren Buckler
contributor.authorSi, Jennie
contributor.authorWunsch, Donald C.
contributor.deptlabApplied Computational Intelligence Laboratory
contributor.deptlabElectrical and Computer Engineering
contributor.deptlabIntelligent Systems Center
subject.LCSHAutomatic programming (Computer science).
subject.LCSHControl theory.
subject.LCSHDynamic programming.
subject.LCSHMachine learning.
subject.LCSHSystems engineering.
date.issued2004
publisherWiley-Interscience
identifier.URI
http://www.worldcat.org/oclc/56416804
identifier.citationJennie Si, Andrew G. Barto, Warren Buckler Powell, and Donald C. Wunsch II. Handbook of Learning and Approximate Dynamic Programming. Piscataway, NJ : Wiley-Interscience, 2004
description.tableOfContents1. 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.
typeBook
type.DCMITypetext
rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
date.available2008-06-19T19:51:33Z
identifier.persist.URI
http://scholarsmine.mst.edu/publication/HandbookofLearningandApproximateDynamicProg_09007dcc80516a36.html