本文介绍了感知信息价值实现多智能体强化学习（SVR-MARL）框架，该框架专为多自主水下航行器（AUV）的协作任务设计。该框架解决了水下环境中感知能力有限和通信风险的挑战，因为主动感知和声学通信会增加暴露的风险。SVR-MARL旨在通过学习考虑实际通信约束和感知信息效用的分布式策略来优化协作任务效率，并在协作定位和跟踪的案例研究中进行了演示。 AI
影响 这项研究可以提高自主水下航行器协同操作的效率和隐蔽性。
排序理由 该集群包含一篇详细介绍多智能体强化学习新框架的研究论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2607.13880v1 Announce Type: new Abstract: In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete information, since active sensing and frequent communications increase the risk of ex…
In underwater covert cooperative missions, autonomous underwater vehicles (AUVs) often cannot rely on active sonar to continuously obtain complete information, since active sensing and frequent communications increase the risk of exposure. As a result, AUVs primarily rely on pass…