Handbook of Learning and Approximate Dynamic Programming (Hardcover)
Language: English
Published by John Wiley & Sons Inc, New York, 2004
- First Edition
- Hardcover
- New
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
AbeBooks seller since June 29, 2022
Condition: New
£ 186.49
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Add to basketItem description from seller
Hardcover. A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation codeProvides a tutorial that readers can use to start implementing the learning algorithms provided in the bookIncludes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implementedThe contributors are leading researchers in the field ADP 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Seller Inventory # 9780471660545
- Title
- Handbook of Learning and Approximate Dynamic Programming (Hardcover)
- Author
- Jennie Si
- Publisher
- John Wiley & Sons Inc, New York
- Publication year
- 2004
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 047166054X
- ISBN 13
- 9780471660545
- Edition
- 1st Edition
- A complete resource to Approximate Dynamic Programming (ADP), including on-line simulation code
- Provides a tutorial that readers can use to start implementing the learning algorithms provided in the book
- Includes ideas, directions, and recent results on current research issues and addresses applications where ADP has been successfully implemented
- The contributors are leading researchers in the field
"Synopsis" may belong to another edition of this title.
About the Author
ANDREW G. BARTO is Professor of Computer Science, University of Massachusetts, Amherst. He is co-director of the Autonomous Learning Laboratory, which carries out interdisciplinary research on machine learning and modeling of biological learning. He is a core faculty member of the Neuroscience and Behavior Program of the University of Massachusetts and was the co-chair for the 2002 NSF Workshop on Learning and Approximate Dynamic Programming. He currently serves as an associate editor of Neural Computation.
WARREN B. POWELL is Professor of Operations Research and Financial Engineering at Princeton University. He is director of CASTLE Laboratory, which focuses on real-time optimization of complex dynamic systems arising in transportation and logistics.
DONALD C. WUNSCH is the Mary K. Finley Missouri Distinguished Professor in the Electrical and Computer Engineering Department at the University of Missouri, Rolla. He heads the Applied Computational Intelligence Laboratory and also has a joint appointment in Computer Science, and is President-Elect of the International Neural Networks Society.
"About the title" may belong to another edition of this title.
CitiRetail
Stevenage, United Kingdom
AbeBooks seller since June 29, 2022
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