English(EN) MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control

新的MAD-PINN框架增强了多智能体控制的安全性和性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-07 04:00

研究人员开发了MAD-PINN，一个利用物理信息神经网络的新型去中心化框架，以应对多智能体控制系统中安全性和性能协同优化的挑战。该方法重新构建了多智能体状态约束最优控制问题（MASC-OCP），并通过在缩减智能体系统上进行训练来实现可扩展性来逼近其解。MAD-PINN结合了基于Hamilton-Jacobi可达性的策略来优先处理安全关键交互，以及用于自适应决策的递推视界策略，在导航任务中展示了优越的性能和安全性权衡。 AI

影响该框架有望在复杂的多智能体环境中实现更鲁棒、更安全自主的系统。

排序理由该集群包含一篇详细介绍多智能体控制新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Manan Tayal, Aditya Singh, Shishir Kolathaya, Somil Bansal · 2026-07-07 04:00

MAD-PINN：一种用于安全最优多智能体控制的去中心化物理信息机器学习框架

arXiv:2509.23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge. Existing approaches based on multi-agent reinforcement learning (MARL), safety filtering, or Model Predictive Control…

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MAD-PINN：一种用于安全最优多智能体控制的去中心化物理信息机器学习框架

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