Riemannian regularization reshapes the policy optimization landscape to enable learning of Kalman gains from data under unknown and rank-deficient covariances with non-asymptotic convergence guarantees.
On the identification of noise covariances and adaptive Kalman filtering: A new look at a 50 year-old problem
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Learning Kalman Policy for Singular Unknown Covariances via Riemannian Regularization
Riemannian regularization reshapes the policy optimization landscape to enable learning of Kalman gains from data under unknown and rank-deficient covariances with non-asymptotic convergence guarantees.