HIGHLIGHTS
- who: NATHAN A. SPIELBERG et al. from the Department of Mechanical Engineering, Stanford University, Stanford, CA, USA have published the article: Learning Policies for Automated Racing Using Vehicle Model Gradients, in the Journal: (JOURNAL)
- what: Inspired by how skilled drivers learn the authors demonstrate improvement from an initial optimization-generated racing trajectory using model-based reinforcement learning. By using a simple physics-based dynamics model gradients of the performance objective the authors show that a full-scale automated race car is capable of improving lap time in experiments on high- low-friction race tracks. Rather . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.