1. Stochastic Learning and Optimization:  A Sensitivity-Based Approach,              Springer, 2007

      A simple version of the contents of this book:

• Preface
• Chapter 1: Introduction

      Part  I  Four Disciplines in Learning and Optimization (Chapter 2 ~ Chapter 7)

• Chapter 2: Perturbation Analysis
• Chapter 3: Learning and Optimization with Perturbation Analysis
• Chapter 4: Markov Decision Processes
• Chapter 5: Sample-Path-Based Policy Iteration
• Chapter 6: Reinforcement Learning
• Chapter 7: Adaptive Control Problems as MDPs

      Part  II The Event-Based Optimization -- A New Approach (Chapter 8 ~ Chapter 9)

• Chapter 8: Event-Based Optimization - A New Approach
• Chapter 9: Constructing Sensitivity Formulas

      Part  III Appendices: Mathematical Background

• Appendix A: Probability and Markov Processes
• Appendix B: Stochastic Matrices
• Appendix C: Queueing Theory
• Notation and Abbreviations
• References
• Index