Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea

• URL: http://arxiv.org/abs/2402.08502v2
• Date: Thu, 16 May 2024 21:14:14 GMT
• Title: Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea
• Authors: Hanna Krasowski, Matthias Althoff,
• Abstract summary: Reinforcement learning (RL) is a promising method to find motion plans for autonomous vehicles. Our approach accomplishes guaranteed rule-compliance by integrating temporal logic specifications into RL. In numerical evaluations on critical maritime traffic situations, our agent always complies with the formalized legal rules and never collides.
• Score: 8.017543518311196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For safe operation, autonomous vehicles have to obey traffic rules that are set forth in legal documents formulated in natural language. Temporal logic is a suitable concept to formalize such traffic rules. Still, temporal logic rules often result in constraints that are hard to solve using optimization-based motion planners. Reinforcement learning (RL) is a promising method to find motion plans for autonomous vehicles. However, vanilla RL algorithms are based on random exploration and do not automatically comply with traffic rules. Our approach accomplishes guaranteed rule-compliance by integrating temporal logic specifications into RL. Specifically, we consider the application of vessels on the open sea, which must adhere to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). To efficiently synthesize rule-compliant actions, we combine predicates based on set-based prediction with a statechart representing our formalized rules and their priorities. Action masking then restricts the RL agent to this set of verified rule-compliant actions. In numerical evaluations on critical maritime traffic situations, our agent always complies with the formalized legal rules and never collides while achieving a high goal-reaching rate during training and deployment. In contrast, vanilla and traffic rule-informed RL agents frequently violate traffic rules and collide even after training.

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