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English(EN) Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach

新的MARL方法增强了水下无人机协同目标跟踪能力

作者 PulseAugur 编辑部 · [3 个来源] ·

两篇新研究论文介绍了用于自主水下航行器（AUV）网络协同目标跟踪的先进多智能体强化学习（MARL）技术。第一篇论文SDA-MARL提出了一种分层架构和一种扩散辅助算法，以解决策略非平稳性、学习效率低下和策略漂移问题。第二篇论文VGG-MADiffRL在分层控制框架内提出了一种价值梯度引导的方法，以克服高维状态-动作建模和对噪声敏感的策略等挑战。两种方法在模拟水下环境中都显示出改进的收敛性、跟踪精度和稳定性。 AI

影响 这些先进的MARL技术有望提高自主水下航行器在复杂环境中协同作业的效率和准确性。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了多智能体强化学习的新算法。

在 arXiv cs.MA (Multiagent) 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新的MARL方法增强了水下无人机协同目标跟踪能力

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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Jiaao Ma, Chuan Lin, Guangjie Han, Shengchao Zhu, Qian Zhu, Ying Liu, Zhenyu Wang ·

    基于多AUV自组网的目标跟踪：一种价值梯度引导的多智能体扩散强化学习方法

    arXiv:2608.12436v1 Announce Type: new Abstract: Multi-AUV ad-hoc network-based target tracking requires networked autonomous underwater vehicles (AUVs) to cooperatively track maneuvering targets under constrained acoustic communication, dynamic topology, and uncertain ocean distu…

  2. arXiv cs.LG TIER_1 English(EN) · Jiaao Ma, Chuan Lin, Guangjie Han, Shengchao Zhu, Zhenyu Wang, Chen An ·

    基于水下移动代理网络的扩散引导协同策略学习用于目标跟踪

    arXiv:2603.29426v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) provides a promising solution for cooperative target tracking in networks of autonomous underwater vehicles (AUVs). However, existing methods still face three major challenges: 1) …

  3. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Zhenyu Wang ·

    基于多AUV自组网的目标跟踪：一种价值梯度引导的多智能体扩散强化学习方法

    Multi-AUV ad-hoc network-based target tracking requires networked autonomous underwater vehicles (AUVs) to cooperatively track maneuvering targets under constrained acoustic communication, dynamic topology, and uncertain ocean disturbances. Although multi-agent reinforcement lear…

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