Convergence and Sample Complexity of Policy Gradient Methods for Stabilizing Linear Systems

Understand

System stabilization via policy gradient (PG) methods has drawn increasing attention in both control and machine learning communities.

• In this paper, we study their convergence and sample complexity for stabilizing linear time-invariant systems in terms of the number of system rollouts.
• Our analysis is built upon a discounted linear quadratic regulator (LQR) method which alternatively updates the policy and the discount factor of the LQR problem.
• Firstly, we propose an explicit rule to adaptively adjust the discount factor by exploring the stability margin of a linear control policy.