Approximate Robust Control of Uncertain Dynamical Systems

Abstract

This work studies the design of safe control policies for large-scale non-linear systems operating in uncertain environments. In such a case, the robust control framework is a principled approach to safety that aims to maximize the worst-case performance of a system. However, the resulting optimization problem is generally intractable for non-linear systems with continuous states. To overcome this issue, we introduce two tractable methods that are based either on sampling or on a conservative approximation of the robust objective. The proposed approaches are applied to the problem of autonomous driving.

Publication
Submitted to NIPS Workshop on Machine Learning for Intelligent Transportation Systems
Date
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Edouard Leurent
PhD Student in Reinforcement Learning