"This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self–contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential."
Zentralblatt fur Mathematik
". . . it is of great value to advanced–level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up–to–date, unified, and rigorous treatment of theoretical and computational aspects of discrete–time Markov decision processes."
Journal of the American Statistical Association
2. Model Formulation.
4. Finite–Horizon Markov Decision Processes.
5. Infinite–Horizon Models: Foundations.
6. Discounted Markov Decision Problems.
7. The Expected Total–Reward. Criterion.
8. Average Reward and Related Criteria.
9. The Average Reward Criterion–Multichain and Communicating Models.
10. Sensitive Discount Optimality.
11. Continuous–Time Models.
Appendix A. Markov Chains.
Appendix B. Semicontinuous Functions.
Appendix C. Normed Linear Spaces.
Appendix D. Linear Programming.