Risk-Averse Control of Undiscounted Transient Markov Models
Optimization and Control
2014-03-25 v2
Abstract
We use Markov risk measures to formulate a risk-averse version of the undiscounted total cost problem for a transient controlled Markov process. We derive risk-averse dynamic programming equations and we show that a randomized policy may be strictly better than deterministic policies, when risk measures are employed. We illustrate the results on an optimal stopping problem and an organ transplant problem.
Keywords
Cite
@article{arxiv.1203.5437,
title = {Risk-Averse Control of Undiscounted Transient Markov Models},
author = {Ozlem Cavus and Andrzej Ruszczynski},
journal= {arXiv preprint arXiv:1203.5437},
year = {2014}
}