State augmentation converts static risk measures on total cost into dynamic programs, yielding sample-complexity bounds for risk-averse MDPs and stochastic optimal control under φ-divergence robustness.
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 converts static risk measures on total cost into dynamic programs, yielding sample-complexity bounds for risk-averse MDPs and stochastic optimal control under φ-divergence robustness.