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Risk-averse formulations of Stochastic Optimal Control and Markov Decision Processes (2505.16651v1)
Published 22 May 2025 in math.OC, math.ST, and stat.TH
Abstract: The aim of this paper is to investigate risk-averse and distributionally robust modeling of Stochastic Optimal Control (SOC) and Markov Decision Process (MDP). We discuss construction of conditional nested risk functionals, a particular attention is given to the Value-at-Risk measure. Necessary and sufficient conditions for existence of non-randomized optimal policies in the framework of robust SOC and MDP are derived. We also investigate sample complexity of optimization problems involving the Value-at-Risk measure.
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