分布式扩散模型解决多智能体协同控制难题，抗延迟且高效。
Distributed Model-Based Diffusion: Finite Horizon Contraction under Bounded Delay
- 基于采样式模型预测控制，实现分布式非线性多智能体协同优化。
- 在有延迟条件下仍保持收敛性，使圆环交换任务完成时间减少31%。
- 适用于真实场景的复杂约束，适合自动驾驶与无人机编队等应用。
同时优化多个智能体的轨迹是受非线性、非凸性及维度灾难困扰的难题。例如交叉路口的飞行器或自动驾驶汽车组成的复杂多智能体系统，若无简化假设则难以求解。智能体间通信延迟进一步加剧挑战。本文分析了分布式模型基扩散（Distributed Model-Based Diffusion）——一种适用于高度非线性、非凸、非光滑多智能体系统的采样式模型预测控制方法。我们证明该方法在有限时域下对延迟具有收缩性与鲁棒性，适用于真实世界约束。在圆环交换任务、中保真度驾驶协作任务和空中对抗场景中进行测试。尽管存在延迟，算法仍使圆环交换任务完成时间缩短31%，空中对抗胜率提升25%，优于集中式模型基扩散。
原文摘要 · Abstract (English)
Simultaneously optimizing the trajectories of multiple agents is a challenging problem plagued by nonlinearity, nonconvexity, and the curse of dimensionality. A collection of interacting aerial vehicles or self-driving cars in an intersection are examples of complex multi-agent systems that remain difficult to solve without many simplifying assumptions. The presence of communication latency between agents further increases the difficulty. In this paper, we analyze Distributed Model-Based Diffusion: a sampling-based Model-Predictive Control method suitable for highly nonlinear, nonconvex, nonsmooth, multi-agent systems. We prove contraction and robustness to latency for multi-agent, nonconvex problems, showing applicability to real-world constraints. We test the algorithm on a circleswap task, a cooperative medium-fidelity driving task, and in an aerial combat scenario. Despite the addition of latency, our algorithm improves circleswap makespan by 31% and increases aerial combat win rate by 25% compared to centralized Model-Based Diffusion.
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