17 citations · 30 across the 4 of their papers we have counts for
5 papers
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning
Zhehua Zhou, Ozgur S. Oguz, Yi Ren +2
Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process. For implementing saf…
Model Predictive Control with Models of Different Granularity and a Non-uniformly Spaced Prediction Horizon
Tim Brüdigam, Daniel Prader, Dirk Wollherr +1
Horizon length and model accuracy are defining factors when designing a Model Predictive Controller. While long horizons and detailed models have a positive effect on control perfo…
Collision Avoidance with Stochastic Model Predictive Control for Systems with a Twofold Uncertainty Structure
Tim Brüdigam, Jie Zhan, Dirk Wollherr +1
Model Predictive Control (MPC) has shown to be a successful method for many applications that require control. Especially in the presence of prediction uncertainty, various types o…
Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing
Tim Brüdigam, Alexandre Capone, Sandra Hirche +2
A fundamental aspect of racing is overtaking other race cars. Whereas previous research on autonomous racing has majorly focused on lap-time optimization, here, we propose a method…
Learning a Low-dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems
Zhehua Zhou, Ozgur S. Oguz, Marion Leibold +1
For safely applying reinforcement learning algorithms on high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning…