Model-Free $μ$-Synthesis: A Nonsmooth Optimization Perspective

no code implementations • 18 Feb 2024 • Darioush Keivan, Xingang Guo, Peter Seiler, Geir Dullerud, Bin Hu

Built upon such a policy optimization persepctive, our paper extends these subgradient-based search methods to a model-free setting.

Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control

no code implementations • 3 Jan 2022 • Aaron Havens, Darioush Keivan, Peter Seiler, Geir Dullerud, Bin Hu

We show that the ROA analysis can be approximated as a constrained maximization problem whose goal is to find the worst-case initial condition which shifts the terminal state the most.

Model-Free $μ$ Synthesis via Adversarial Reinforcement Learning

no code implementations • 30 Nov 2021 • Darioush Keivan, Aaron Havens, Peter Seiler, Geir Dullerud, Bin Hu

We build a connection between robust adversarial RL and $\mu$ synthesis, and develop a model-free version of the well-known $DK$-iteration for solving state-feedback $\mu$ synthesis with static $D$-scaling.

reinforcement-learning Reinforcement Learning (RL)