Handbook of Learning and Approximate Dynamic Programming

✍ Scribed by Jennie Si, Andy Barto, Warren Powell, Donald Wunsch(auth.)

• Publisher: Wiley-IEEE Press
• Year: 2004
• Tongue: English
• Leaves: 651
• Category: Library

✦ 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

Content:
Chapter 1 ADP: Goals, Opportunities and Principles (pages 3–44): Paul Werbos
Chapter 2 Reinforcement Learning and Its Relationship to Supervised Learning (pages 45–63): Andrew G. Barto and Thomas G. Dietterich
Chapter 3 Model?Based Adaptive Critic Designs (pages 65–95): Silvia Ferrari and Robert F. Stengel
Chapter 4 Guidance in the Use of Adaptive Critics for Control (pages 97–124): George G. Lendaris and James C. Neidhoefer
Chapter 5 Direct Neural Dynamic Programming (pages 125–151): Jennie Si, Lei Yang and Derong Liu
Chapter 6 The Linear Programming Approach to Approximate Dynamic Programming (pages 153–178): Daniela Pucci de Farias
Chapter 7 Reinforcement Learning in Large, High?Dimensional State Spaces (pages 179–202): Greg Grudic and Lyle Ungar
Chapter 8 Hierarchical Decision Making (pages 203–232): Malcolm Ryan
Chapter 9 Improved Temporal Difference Methods with Linear Function Approximation (pages 233–259): Dimitri P. Bertsekas, Vivek S. Borkar and Angelia Nedich
Chapter 10 Approximate Dynamic Programming for High?Dimensional Resource Allocation Problems (pages 261–283): Warren B. Powell and Benjamin Van Roy
Chapter 11 Hierarchical Approaches to Concurrency, Multiagency, and Partial Observability (pages 285–310): Sridhar Mahadevan, Mohammad Ghavamzadeh, Khashayar Rohanimanesh and Georgias Theocharous
Chapter 12 Learning and Optimization — From a System Theoretic Perspective (pages 311–335): Xi?Ren Cao
Chapter 13 Robust Reinforcement Learning Using Integral?Quadratic Constraints (pages 337–358): Charles W. Anderson, Matt Kretchmar, Peter Young and Douglas Hittle
Chapter 14 Supervised Actor?Critic Reinforcement Learning (pages 359–380): Michael T. Rosenstein and Andrew G. Barto
Chapter 15 BPTT and DAC — A Common Framework for Comparison (pages 381–404): Danil V. Prokhorov
Chapter 16 Near?Optimal Control Via Reinforcement Learning and Hybridization (pages 405–432): Augustine O. Esogbue and Warren E. Hearnes
Chapter 17 Multiobjective Control Problems by Reinforcement Learning (pages 433–461): Dong?Oh Kang and Zeungnam Bien
Chapter 18 Adaptive Critic Based Neural Network for Control?Constrained Agile Missile (pages 463–478): S. N. Balakrishnan and Dongchen Han
Chapter 19 Applications of Approximate Dynamic Programming in Power Systems Control (pages 479–515): Ganesh K Venayagamoorthy, Donald C Wunsch and Ronald G Harley
Chapter 20 Robust Reinforcement Learning for Heating, Ventilation, and Air Conditioning Control of Buildings (pages 517–534): Charles W. Anderson, Douglas Hittle, Matt Kretchmar and Peter Young
Chapter 21 Helicopter Flight Control Using Direct Neural Dynamic Programming (pages 535–559): Russell Enns and Jennie Si
Chapter 22 Toward Dynamic Stochastic Optimal Power Flow (pages 561–598): James A. Momoh
Chapter 23 Control, Optimization, Security, and Self?healing of Benchmark Power Systems (pages 599–634): James A. Momoh and Edwin Zivi