1 code implementation • 25 Jan 2024 • Leonardo F. Toso, Donglin Zhan, James Anderson, Han Wang
We investigate the problem of learning Linear Quadratic Regulators (LQR) in a multi-task, heterogeneous, and model-free setting.
1 code implementation • 19 Sep 2023 • Leonardo F. Toso, Han Wang, James Anderson
We investigate the problem of learning an $\epsilon$-approximate solution for the discrete-time Linear Quadratic Regulator (LQR) problem via a Stochastic Variance-Reduced Policy Gradient (SVRPG) approach.
1 code implementation • 3 Apr 2023 • Leonardo F. Toso, Han Wang, James Anderson
We address the problem of learning linear system models from observing multiple trajectories from different system dynamics.
1 code implementation • 25 Nov 2022 • Han Wang, Leonardo F. Toso, James Anderson
We study the problem of learning a linear system model from the observations of $M$ clients.