An Approximate Solution Method for Large Risk-Averse Markov Decision Processes
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.
Cite
Text
Petrik and Subramanian. "An Approximate Solution Method for Large Risk-Averse Markov Decision Processes." Conference on Uncertainty in Artificial Intelligence, 2012.Markdown
[Petrik and Subramanian. "An Approximate Solution Method for Large Risk-Averse Markov Decision Processes." Conference on Uncertainty in Artificial Intelligence, 2012.](https://mlanthology.org/uai/2012/petrik2012uai-approximate/)BibTeX
@inproceedings{petrik2012uai-approximate,
title = {{An Approximate Solution Method for Large Risk-Averse Markov Decision Processes}},
author = {Petrik, Marek and Subramanian, Dharmashankar},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2012},
pages = {805-814},
url = {https://mlanthology.org/uai/2012/petrik2012uai-approximate/}
}