The multistage DRRO-LQR problem over linear disturbance-feedback policies admits an exact SDP reformulation whose solution is the nominal certainty-equivalent LQR law plus a strictly causal empirical-mean correction.
Wasserstein distributionally robust regret optimization.arXiv preprint arXiv:2504.10796
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DRRO for RLHF minimizes worst-case regret relative to the best policy under Wasserstein reward perturbations, yielding an exact inner solution and water-filling policy structure for the promptwise simplex model plus a practical policy-gradient algorithm.
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Distributionally Robust Regret Optimal LQR with Common Stage-Law Ambiguity
The multistage DRRO-LQR problem over linear disturbance-feedback policies admits an exact SDP reformulation whose solution is the nominal certainty-equivalent LQR law plus a strictly causal empirical-mean correction.
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Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback
DRRO for RLHF minimizes worst-case regret relative to the best policy under Wasserstein reward perturbations, yielding an exact inner solution and water-filling policy structure for the promptwise simplex model plus a practical policy-gradient algorithm.