Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks
We propose a computational framework for replacing the repeated numerical solution of differential Riccati equations in finite-horizon Linear Quadratic Regulator (LQR) problems by a learned operator surrogate. Instead of solving a nonlinear matrix-valued differential equation for each new system instance, we construct offline an approximation of the associated solution operator mapping time-dependent system parameters to the Riccati trajectory. The resulting model enables fast online evaluation of approximate optimal feedbacks across a wide class of systems, thereby shifting the computational burden from repeated numerical integration to a one-time learning stage. From a theoretical persp...
| Authors | Jun Chen, Umberto Biccari, Junmin Wang |
|---|---|
| Primary category | math.OC |
| Published | 20 April 2026 |
| Last updated at source | 21 April 2026 |
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