Corpus ID: 235899151

Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning

@article{Kamran2021MinimizingSI,
  title={Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning},
  author={Danial Kamran and Tizian Engelgeh and Marvin Busch and Johannes Fischer and Christoph Stiller},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.07316}
}
Despite recent advances in reinforcement learning (RL), its application in safety critical domains like autonomous vehicles is still challenging. Although punishing 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 that can tune their level of conservativity at run-time based on the desired comfort and utility. Using a proactive safety… Expand

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