Reinforcement Learning with Ensemble Model Predictive Safety
  Certification
Sven Gronauer Tom Haider Felippe Schmoeller da Roza Klaus Diepold
Reinforcement learning algorithms need exploration to learn. However,
unsupervised exploration prevents the deployment of such algorithms on
safety-critical tasks and limits real-world deployment. In this paper, we
propose a new algorithm called Ensemble Model Predictive Safety Certification
that combines model-based deep reinforcement learning with tube-based model
predictive control to correct the actions taken by a learning agent, keeping
safety constraint violations at a minimum through planning. Our approach aims
to reduce the amount of prior knowledge about the actual system by requiring
only offline data generated by a safe controller. Our results show that we can
achieve significantly fewer constraint violations than comparable reinforcement
learning methods.