Handbook of Learning and Approximate Dynamic Progr Amming

Si, Jennie

ISBN 10: 047166054X / ISBN 13: 9780471660545

Published by John Wiley & Sons Inc, 2004

About this Item

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. Editor(s): Si, Jennie; Barto, Andrew G.; Powell, Warren B.; Wunsch, Don. Series: IEEE Press Series on Computational Intelligence. Num Pages: 672 pages, Illustrations. BIC Classification: TJ; UY. Category: (P) Professional & Vocational. Dimension: 240 x 160 x 37. Weight in Grams: 1054. . 2004. Mixed media pr. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780471660545

Bibliographic Details

• Title: Handbook of Learning and Approximate Dynamic Progr Amming
• Publisher: John Wiley & Sons Inc
• Publication Date: 2004
• Language: English
• ISBN 10: 047166054X
• ISBN 13: 9780471660545
• Binding: Hardcover
• Condition: New

About this title

Synopsis

• 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

About the Author

JENNIE SI is Professor of Electrical Engineering, Arizona State University, Tempe, AZ. She is director of Intelligent Systems Laboratory, which focuses on analysis and design of learning and adaptive systems. In addition to her own publications, she is the Associate Editor for IEEE Transactions on Neural Networks, and past Associate Editor for IEEE Transactions on Automatic Control and IEEE Transactions on Semiconductor Manufacturing. She was the co-chair for the 2002 NSF Workshop on Learning and Approximate Dynamic Programming.

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.