Stochastic Learning and Optimization

A Sensitivity-Based Approach

By Xi-Ren Cao

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Performance optimization is vital in the design and operation of modern engineering systems. This book provides a unified framework based on a sensitivity point of view. It introduces new approaches and proposes new research topics.

Full Description

  • ISBN13: 978-0-3873-6787-3
  • 586 Pages
  • User Level: Science
  • Publication Date: October 23, 2007
  • Available eBook Formats: PDF
  • eBook Price: $149.00
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Full Description
Performance optimization is vital in the design and operation of modern engineering systems, including communications, manufacturing, robotics, and logistics. Most engineering systems are too complicated to model, or the system parameters cannot be easily identified, so learning techniques have to be applied. This is a multi-disciplinary area which has been attracting wide attention across many disciplines. Areas such as perturbation analysis (PA) in discrete event dynamic systems (DEDSs), Markov decision processes (MDPs) in operations research, reinforcement learning (RL) or neuro-dynamic programming (NDP) in computer science, identification and adaptive control (I&AC) in control systems, share the common goal: to make the 'best decision' to optimize system performance. This book provides a unified framework based on a sensitivity point of view. It also introduces new approaches and proposes new research topics within this sensitivity-based framework.
Table of Contents

Table of Contents

  1. Perturbation Analysis of Queueing Systems.
  2. Perturbation Analysis of Markov Chains.
  3. Performance Sensitivities of Markov Processes.
  4. Learning and Optimization with Perturbation Analysis.
  5. Markov Decision Processes.
  6. Ergodic Systems.
  7. Sample
  8. Path
  9. Based Policy Iteration.
  10. Reinforcement Learning.
  11. Stochastic Approximation.
  12. Temporal Difference Methods.
  13. Identification and Adaptive Control.
  14. Constructing Sensitivity Formulas.
  15. Event
  16. Based Optimization of Markov Systems.
  17. Estimating Aggregated Potentials.
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