Reinforcement Learning with Ensemble Model Predictive Safety Certification

• URL: http://arxiv.org/abs/2402.04182v1
• Date: Tue, 6 Feb 2024 17:42:39 GMT
• Title: Reinforcement Learning with Ensemble Model Predictive Safety Certification
• Authors: Sven Gronauer, Tom Haider, Felippe Schmoeller da Roza, Klaus Diepold
• Abstract summary: unsupervised exploration prevents the deployment of reinforcement learning algorithms on safety-critical tasks. We propose a new algorithm that combines model-based deep reinforcement learning with tube-based model predictive control to correct the actions taken by a learning agent. Our results show that we can achieve significantly fewer constraint violations than comparable reinforcement learning methods.
• Score: 2.658598582858331
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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