Falsification-Driven Reinforcement Learning for Maritime Motion Planning

• URL: http://arxiv.org/abs/2510.06970v1
• Date: Wed, 08 Oct 2025 12:56:31 GMT
• Title: Falsification-Driven Reinforcement Learning for Maritime Motion Planning
• Authors: Marlon Müller, Florian Finkeldei, Hanna Krasowski, Murat Arcak, Matthias Althoff,
• Abstract summary: Compliance with maritime traffic rules is essential for the safe operation of autonomous vessels.<n>Training reinforcement learning (RL) agents to adhere to them is challenging.<n>We propose a falsification-driven RL approach that generates adversarial training scenarios in which the vessel under test violates maritime traffic rules.
• Score: 10.405737384575334
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Compliance with maritime traffic rules is essential for the safe operation of autonomous vessels, yet training reinforcement learning (RL) agents to adhere to them is challenging. The behavior of RL agents is shaped by the training scenarios they encounter, but creating scenarios that capture the complexity of maritime navigation is non-trivial, and real-world data alone is insufficient. To address this, we propose a falsification-driven RL approach that generates adversarial training scenarios in which the vessel under test violates maritime traffic rules, which are expressed as signal temporal logic specifications. Our experiments on open-sea navigation with two vessels demonstrate that the proposed approach provides more relevant training scenarios and achieves more consistent rule compliance.

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