基于证伪驱动的强化学习用于海上运动规划
Falsification-driven reinforcement learning for maritime motion planning
AI总结：
提出一种证伪驱动的强化学习方法，通过生成对抗性训练场景（违反信号时态逻辑规范的海上交通规则）来提升自主船舶的规则遵守能力，实验表明该方法能提供更相关的训练场景并实现更一致的规则遵守。
摘要：
遵守海上交通规则对于自主船舶的安全运行至关重要，但训练强化学习（RL）代理遵守这些规则具有挑战性。RL代理的行为由其遇到的训练场景塑造，但创建能够捕捉海上导航复杂性的场景并非易事，仅靠真实世界数据是不够的。为了解决这个问题，我们提出了一种证伪驱动的RL方法，该方法生成对抗性训练场景，其中被测船舶违反以信号时态逻辑规范表示的海上交通规则。我们在两艘船舶的公海导航实验表明，所提出的方法提供了更相关的训练场景，并实现了更一致的规则遵守。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
