State augmentation allows dynamic programming and sample complexity bounds for MDPs and optimal control under static risk measures including CVaR.
Risk-averse formulations of stochastic optimal control and markov decision processes
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Sample Complexity for Markov Decision Processes and Stochastic Optimal Control with Static Risk Measures
State augmentation allows dynamic programming and sample complexity bounds for MDPs and optimal control under static risk measures including CVaR.