This was part of
Reinforcement Learning Bootcamp
Stochastic Optimal Control of LQG Systems
R. Srikant 2, University of Illinois at Urbana-Champaign
Wednesday, March 11, 2026
Abstract: We will provide a concise introduction to the problem of optimally controlling a linear system driven by white, Gaussian nose, and where the cost function is quadratic. We will comment on similarities and differences with the discrete state and action space MDPs, which are the most common model in the reinforcement literature. Unlike general MDPs, LQG problems with both full information and partial information admit closed-form optimal solutions. The tutorial will present a derivation of these results.