arXiv Machine Learning

Linear Exponential Quadratic Gaussian Covariance Steering

The paper formulates and analyzes the linear exponential quadratic Gaussian (LEQG) covariance steering problem in continuous time over a finite horizon. It shows that the optimal controller, still a linear state feedback, cannot be expressed in closed form but is parameterized by a symmetric matrix solving an algebraic equation that captures the risk‑sensitivity parameter. The authors demonstrate that this controller generalizes the risk‑neutral case and prove existence‑uniqueness of solutions near the known risk‑neutral solution for matched noise and input channels, illustrated with a numerical example.

arXiv Machine Learning
Jun 29

PAC-Bayesian Certificates for Quadratic Closed-Loop Control

arXiv:2606. 28281v1 Announce Type: cross Abstract: PAC-Bayesian bounds provide finite-sample guarantees for data-dependent randomized predictors, but applying them to learning-based control is difficult because the natural objective is a quadratic trajectory cost.

By Domagoj Herceg
arXiv Machine Learning
Jul 27

Trajectory-Regularized Stochastic Optimal Control via KL Divergence

arXiv:2607. 22201v1 Announce Type: cross Abstract: We introduce trajectory-regularized stochastic optimal control (TRSOC), which augments standard stochastic optimal control (SOC) with a Kullback--Leibler (KL) divergence between controlled and reference trajectory distributions.

By Mintae Kim, Koushil Sreenath