title: Stabilizing Dynamical Systems via Policy Gradient Methods
abstract: Stabilizing an unknown control system is one of the most fundamental problems in control systems engineering.  In this paper, we provide a simple, model-free algorithm for stabilizing fully observed dynamical systems.  While model-free methods have become increasingly popular in practice due to their simplicity and flexibility, stabilization via direct policy search has received surprisingly little attention. Our algorithm proceeds by solving a series of discounted LQR problems, where the discount factor is gradually increased. We prove that this method efficiently recovers a stabilizing controller for linear systems, and for smooth, nonlinear systems within a neighborhood of their equilibria. Our approach overcomes a significant limitation of prior work, namely the need for a pre-given stabilizing control policy. 
  We empirically evaluate the effectiveness of our approach on common control benchmarks. 
  
authors: Juan Carlos Perdomo Jack Umenberger Max Simchowitz
hIndex: Max Simchowitz 18