Handbook of Learning and Approximate Dynamic Programming

Copyright © 2004 the Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Handbook of Learning and Approximate Dynamic Programming

Author(s): Jennie Si, Andy Barto, Warren Powell, Donald Wunsch

Published Online: 29 MAY 2012 08:26AM EST

Print ISBN: 9780471660545

Online ISBN: 9780470544785

DOI: 10.1002/9780470544785

About this Book

Product Information

About The Product
  • 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

Table of contents

    1. You have free access to this content
      Front Matter (pages i–xxi)

    1. Foreword (pages 1–2)

      Shankar Sastry

    1. Chapter 1

      ADP: Goals, Opportunities and Principles (pages 3–44)

      Paul Werbos

  4. Part I: Overview

    1. Chapter 2

      Reinforcement Learning and Its Relationship to Supervised Learning (pages 45–63)

      Andrew G. Barto and Thomas G. Dietterich

    2. Chapter 3

      Model-Based Adaptive Critic Designs (pages 65–95)

      Silvia Ferrari and Robert F. Stengel

    3. Chapter 4

      Guidance in the Use of Adaptive Critics for Control (pages 97–124)

      George G. Lendaris and James C. Neidhoefer

    4. Chapter 5

      Direct Neural Dynamic Programming (pages 125–151)

      Jennie Si, Lei Yang and Derong Liu

    5. Chapter 6

      The Linear Programming Approach to Approximate Dynamic Programming (pages 153–178)

      Daniela Pucci de Farias

    6. Chapter 7

      Reinforcement Learning in Large, High-Dimensional State Spaces (pages 179–202)

      Greg Grudic and Lyle Ungar

    7. Chapter 8

      Hierarchical Decision Making (pages 203–232)

      Malcolm Ryan

  5. Part II: Technical Advances

    1. Chapter 9

      Improved Temporal Difference Methods with Linear Function Approximation (pages 233–259)

      Dimitri P. Bertsekas, Vivek S. Borkar and Angelia Nedich

    2. Chapter 10

      Approximate Dynamic Programming for High-Dimensional Resource Allocation Problems (pages 261–283)

      Warren B. Powell and Benjamin Van Roy

    3. Chapter 11

      Hierarchical Approaches to Concurrency, Multiagency, and Partial Observability (pages 285–310)

      Sridhar Mahadevan, Mohammad Ghavamzadeh, Khashayar Rohanimanesh and Georgias Theocharous

    4. Chapter 12

      Learning and Optimization — From a System Theoretic Perspective (pages 311–335)

      Xi-Ren Cao

    5. Chapter 13

      Robust Reinforcement Learning Using Integral-Quadratic Constraints (pages 337–358)

      Charles W. Anderson, Matt Kretchmar, Peter Young and Douglas Hittle

    6. Chapter 14

      Supervised Actor-Critic Reinforcement Learning (pages 359–380)

      Michael T. Rosenstein and Andrew G. Barto

    7. Chapter 15

      BPTT and DAC — A Common Framework for Comparison (pages 381–404)

      Danil V. Prokhorov

  6. Part III: Applications

    1. Chapter 16

      Near-Optimal Control Via Reinforcement Learning and Hybridization (pages 405–432)

      Augustine O. Esogbue and Warren E. Hearnes

    2. Chapter 17

      Multiobjective Control Problems by Reinforcement Learning (pages 433–461)

      Dong-Oh Kang and Zeungnam Bien

    3. Chapter 18

      Adaptive Critic Based Neural Network for Control-Constrained Agile Missile (pages 463–478)

      S. N. Balakrishnan and Dongchen Han

    4. Chapter 19

      Applications of Approximate Dynamic Programming in Power Systems Control (pages 479–515)

      Ganesh K Venayagamoorthy, Donald C Wunsch and Ronald G Harley

    5. 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

    6. Chapter 21

      Helicopter Flight Control Using Direct Neural Dynamic Programming (pages 535–559)

      Russell Enns and Jennie Si

    7. Chapter 22

      Toward Dynamic Stochastic Optimal Power Flow (pages 561–598)

      James A. Momoh

    8. Chapter 23

      Control, Optimization, Security, and Self-healing of Benchmark Power Systems (pages 599–634)

      James A. Momoh and Edwin Zivi

    1. You have free access to this content
      Bibliography (pages 635–637)

    1. You have free access to this content
      Index (pages 639–644)

SEARCH