International Series in Operations Research & Management Science, 40
1. Introduction; E.A. Feinberg, A. Shwartz. Part I: Finite State and Action Models. 2. Finite State and Action MDPs; L. Kallenberg. 3. Bias Optimality; M.E. Lewis, M.L. Puterman. 4. Singular Perturbations of Markov Chains and Decision Processes; K.E. Avrachenkov, et al. Part II: Infinite State Models. 5. Average Reward Optimization Theory for Denumerable State Spaces; L.I. Sennott. 6. Total Reward Criteria; E.A. Feinberg. 7. Mixed Criteria; E.A. Feinberg, A. Shwartz. 8. Blackwell Optimality; A. Hordijk, A.A. Yushkevich. 9. The Poisson Equation for Countable Markov Chains: Probabilistic Methods and Interpretations; A.M. Makowski, A. Shwartz. 10. Stability, Performance Evaluation, and Optimization; S.P. Meyn. 11. Convex Analytic Methods in Markov Decision Processes; V.S. Borkar. 12. The Linear Programming Approach; O. Hernandez-Lerma, J.B. Lasserre. 13. Invariant Gambling Problems and Markov Decision Processes; L.E. Dubins, et al. Part III: Applications. 14. Neuro-Dynamic Programming: Overview and Recent Trends; B. Van Roy. 15. Markov Decision Processes in Finance and Dynamic Options; M. Schal. 16. Applications of Markov Decision Processes in Communication Networks; E. Altman. 17. Water Reservoir Applications of Markov Decision Processes; B.F. Lamond, A. Boukhtouta. Index.
1.1 AN OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming-studiessequential optimization ofdiscrete time stochastic systems.