PAC-Bayesian Certificates for Quadratic Closed-Loop Control

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 whose losses are unbounded and non-Lipschitz.

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. Such losses are unbounded, non-Lipschitz , and lead to…