From Theory to Robotics: Applying Sums-of-Squares Optimization for Better Control

Reinforcement learning has exhibited notable empirical success in approximating solutions to the Hamilton-Jacobi-Bellman (HJB) equation, consequently generating highly dynamic controllers. However, the inability to bind the suboptimality of resulting controllers or the approximation quality of the true cost-to-go function due to finite sampling and function approximators has limited the broader application of such methods.  Consequently, […]

The post From Theory to Robotics: Applying Sums-of-Squares Optimization for Better Control appeared first on MarkTechPost.

ai paper summary ai shorts applications approximation artificial intelligence control cost dynamic editors pick equation function hamilton however optimization quality reinforcement reinforcement learning robotics sampling solutions squares staff success tech news technology theory true