Tightness of the robust control leading term
Derive a matching lower bound for the robust control formulation K_RC(G) = argmin_K sup_{θ∈G} (C(K, θ) − C(K(θ), θ)) in the LQR learning setting, proving that the 1/N leading term in the upper bound—proportional to dθ‖H(θ⋆)FI(θ⋆)^{-1}‖—is tight.
References
While we lack a lower bound for robust control, we conjecture that the leading term is tight.
— Domain Randomization is Sample Efficient for Linear Quadratic Control
(2502.12310 - Fujinami et al., 17 Feb 2025) in Section 3.2 Sample Efficiency of Robust Control