Handbook of Learning and Approximate Dynamic Programming

Front Cover
Jennie Si
John Wiley & Sons, 2004 - Computers - 644 pages
2 Reviews
  • WileyPLUS is a research-based online environment for effective teaching and learning. WileyPLUS is packed with interactive study tools and resources-including the complete online textbook-to give your students more value for their money.
  • WileyPLUS is now equipped with an adaptive learning module called ORION. Based on cognitive science, WileyPLUS with ORION, provides students with a personal, adaptive learning experience so they can build their proficiency on topics and use their study time most effectively. WileyPLUS with ORION helps students learn by learning about them.
  • The Flying Circus of Physics, written by Jearl Walker, is incorporated into sample problems, text examples and end-of-chapter problems providing interesting real-world physics.
  • Reading questions (available online) help test for reading comprehension
  • Checkpoints offer stopping points so students can check their understanding of a question.
  • Sample problems demonstrate how problems can be solved with reasoned solutions rather than quick and simplistic plugging of numbers into an equation with no regard for what the equation means.

What people are saying - Write a review

We haven't found any reviews in the usual places.


Reinforcement Learning and Its Relationship to Supervised Learning
ModelBased Adaptive Critic Designs
Guidance in the Use of Adaptive Critics for Control
Direct Neural Dynamic Programming
The Linear Programming Approach to Approximate Dynamic
g Discussion
Reinforcement Learning in Large HighDimensional State Spaces
g Conclusion
Hierarchical Approaches to Concurrency Multiagency
Learning and Optimization From a System Theoretic Perspective
Robust Reinforcement Learning Using IntegralQuadratic
Supervised ActorCritic Reinforcement Learning
NearOptimal Control Via Reinforcement Learning
Multiobjective Control Problems by Reinforcement Learning
Adaptive Critic Based Neural Network for ControlConstrained

g Hierarchical Decision Making
Hierarchical Remforcement Learning in Theory
Hierarchical Remforcement Learning in Practice
IntraBehavior Learmng
Improved Temporal Difference Methods with Linear Function
Approximate Dynamic Programming for HighDimensional
Applications of Approximate Dynamic Programming in Power Systems
Robust Reinforcement Learning for floating Ventilation
Helicopter Flight Control Using Direct Neural Dynamic Programming
Toward Dynamic Stochastic Optimal Power Flow
Control Optimization Security and Selfhealing of Benchmark

Common terms and phrases

References to this book

All Book Search results »

About the author (2004)

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.

Bibliographic information