Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning
Danial Kamran,Tizian Engelgeh,Marvin Busch,Johannes Fischer,Christoph Stiller,Danial Kamran,Tizian Engelgeh,Marvin Busch,Johannes Fischer,Christoph Stiller
Despite recent advances in reinforcement learning (RL), its application in safety critical domains like autonomous vehicles is still challenging. Although penalizing RL agents for risky situations can help to learn safe policies, it may also lead to highly conservative behavior. In this paper, we propose a distributional RL framework in order to learn adaptive policies which allow to tune their le...
