  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.
