Risk-Sensitive and Mean Variance Optimality in Markov Decision Processes

Abstract

In this note, we compare two approaches for handling risk-variability features arising in discrete-time Markov decision processes: models with exponential utility functions and mean variance optimality models. Computational approaches for finding optimal decision with respect to the optimality criteria mentioned above are presented and analytical results showing connections between the above optimality criteria are discussed.

Cite this paper

@inproceedings{Sladk2008RiskSensitiveAM, title={Risk-Sensitive and Mean Variance Optimality in Markov Decision Processes}, author={Karel Sladk{\'y} and Milan Sitar}, year={2008} }