Institute for Systems Research

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Simulation-Based Algorithms for Average Cost Markov Decision Processes
(1999) He, Ying; Fu, Michael C.; Marcus, Steven I.; Fu, Michael C.; Marcus, Steven I.; ISR
In this paper, we give a summary of recent development of simulation-based algorithmsfor average cost MDP problems, which are different from those for discounted cost problems or shortest pathproblems. We introduce both simulation-based policy iteration algorithms and simulation-based value iterationalgorithms for average cost problems, and give the pros and cons of each algorithm.
Risk Sensitive Control of Markov Processes in Countable State Space
(1996) Hernandez-Hernandez, Daniel; Marcus, Steven I.; ISR
In this paper we consider infinite horizon risk-sensitive control of Markov processes with discrete time and denumerable state space. This problem is solved proving, under suitable conditions, that there exists a bounded solution to the dynamic programming equation. The dynamic programming equation is transformed into an Isaacs equation for a stochastic game, and the vanishing discount method is used to study its solution. In addition, we prove that the existence conditions are as well necessary.