AI RESEARCH
Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks
arXiv CS.LG
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ArXi:2604.18507v1 Announce Type: cross 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.