Reinforcement Learning with Ensemble Model Predictive Safety Certification
<|reference_start|>Reinforcement Learning with Ensemble Model Predictive Safety Certification: 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.<|reference_end|>
@article{gronauer2024reinforcement,
    title={Reinforcement Learning with Ensemble Model Predictive Safety
  Certification},
    author={Sven Gronauer, Tom Haider, Felippe Schmoeller da Roza, Klaus Diepold},
    journal={arXiv preprint arXiv:2402.04182},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04182},
    primaryClass={cs.LG cs.RO}
}