Uncertainty in Artificial Intelligence
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An Approximate Solution Method for Large Risk-Averse Markov Decision Processes
Marek Petrik, Dharmashankar Subramanian
Stochastic domains often involve risk-averse decision makers. While recent work has focused on how to model risk in Markov decision processes using risk measures, it has not addressed the problem of solving large risk-averse formulations. In this paper, we propose and analyze a new method for solving large risk-averse MDPs with hybrid continuous-discrete state spaces and continuous action spaces. The proposed method iteratively improves a bound on the value function using a linearity structure of the MDP. We demonstrate the utility and properties of the method on a portfolio optimization problem.
Pages: 805-814
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PDF Link: /papers/12/p805-petrik.pdf
AUTHOR = "Marek Petrik and Dharmashankar Subramanian",
TITLE = "An Approximate Solution Method for Large Risk-Averse Markov Decision Processes",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "805--814"

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