Model-Free Output Feedback Stabilization via Policy Gradient Methods
The work proposes a model-free method to learn stabilizing output feedback controllers for unstable discrete-time linear systems without knowing the system model. It extends policy gradient (PG) methods by using zeroth-order updates based on system trajectories and a discount mechanism to handle partial observability. This framework successfully finds a stabilizing policy by iteratively improving the controller.