Algorithmic Optimality Guarantees for Nonsmooth $H_\infty$ Output-Feedback Policy Search
We study continuous-time full-order dynamic output-feedback $H_\infty$ policy search, a nonconvex and nonsmooth problem. Direct policy search is a central paradigm in reinforcement learning and continuous control, but rigorous guarantees remain scarce in robust output-feedback settings. The $H_\infty$ problem is a canonical benchmark because it captures disturbance attenuation and robustness while exposing the hard nonsmooth geometry of policy-space optimization. We prove that on the exact identity-gauge slice of the extended convex lift, $\varepsilon$-stationarity yields $O(\varepsilon)$-suboptimality on compact exact slices, which in turn yields convergence-rate guarantees for nonsmooth...
| Authors | Ashkan Soleymani, Patrick Jaillet |
|---|---|
| Primary category | math.OC |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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