An Approximate Solution Method for Large Risk-Averse Markov Decision Processes

Marek Petrik and Dharmashankar Subramanian

Papers from arXiv.org

Abstract: 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.

Date: 2012-10
New Economics Papers: this item is included in nep-rmg and nep-upt
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Citations: View citations in EconPapers (1)

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