Sinkhorn divergence defines ambiguity sets that make distributionally robust linear quadratic control over linear policies solvable via convex programming even with safety constraints.
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Combining gradient-based policy optimization with recursive system identification in differentiable MPC ensures convergence to an optimal controller under model uncertainty.
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Sinkhorn Ambiguity Sets for Distributionally Robust Control: Convexity, Weak Compactness, and Tractability
Sinkhorn divergence defines ambiguity sets that make distributionally robust linear quadratic control over linear policies solvable via convex programming even with safety constraints.
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Policy Optimization with Differentiable MPC: Convergence Analysis under Uncertainty
Combining gradient-based policy optimization with recursive system identification in differentiable MPC ensures convergence to an optimal controller under model uncertainty.