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• W2949207039 abstract "In many sequential decision-making problems we may want to manage risk by minimizing some measure of variability in costs in addition to minimizing a standard criterion. Conditional value-at-risk (CVaR) is a relatively new risk measure that addresses some of the shortcomings of the well-known variance-related risk measures, and because of its computational efficiencies has gained popularity in finance and operations research. In this paper, we consider the mean-CVaR optimization problem in MDPs. We first derive a formula for computing the gradient of this risk-sensitive objective function. We then devise policy gradient and actor-critic algorithms that each uses a specific method to estimate this gradient and updates the policy parameters in the descent direction. We establish the convergence of our algorithms to locally risk-sensitive optimal policies. Finally, we demonstrate the usefulness of our algorithms in an optimal stopping problem." @default.
• W2949207039 created "2019-06-27" @default.
• W2949207039 creator A5013843778 @default.
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• W2949207039 date "2014-06-12" @default.
• W2949207039 modified "2023-09-27" @default.
• W2949207039 title "Algorithms for CVaR Optimization in MDPs" @default.
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• W2949207039 hasPublicationYear "2014" @default.
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