Papers › On the Global Optimality of Direct Policy Search for Nonsmooth H_∞ Output-Feedback Control
On the Global Optimality of Direct Policy Search for Nonsmooth H_∞ Output-Feedback Control
Yujie Tang, Yang Zheng
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Direct policy search has achieved great empirical success in reinforcement learning. Recently, there has been increasing interest in studying its theoretical properties for continuous control, and fruitful results have been established for linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG) control that are smooth and nonconvex. In this paper, we consider the standard H_∞ robust control for output feedback systems and investigate the global optimality of direct policy search. Unlike LQR or LQG, the H_∞ cost function is nonsmooth in the policy space. Despite the lack of smoothness and convexity, our main result shows that for a class of non-degenerated stabilizing controllers, all Clarke stationary points of H_∞ robust control are globally optimal and there is no spurious local minimum. Our proof technique is motivated by the idea of differentiable convex liftings (DCL), and we extend DCL to analyze the nonsmooth and nonconvex H_∞ robust control via convex reformulation. Our result sheds some light on the analysis of direct policy search for solving nonsmooth and nonconvex robust control problems.
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