GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systems

AI 总结

• 针对多智能体系统中的部分可观测难题，作者指出现有信念估计与通信方法在利用全局信息和辅助信息建模上存在不足。
• 提出Global State Diffusion Algorithm（GlobeDiff），基于局部观测推断全局状态，并将推断过程形式化为多模态扩散过程。
• 方法可缓解状态估计歧义，实现高保真全局状态重建，适用于更复杂的不确定环境。
• 论文在单峰与多峰分布设定下都给出了GlobeDiff估计误差可界定的理论保证。
• 大量实验表明GlobeDiff在性能上优于对比方法，且能更准确地恢复全局状态。

内容摘录

In the realm of multi-agent systems, the challenge of \emph{partial observability} is a critical barrier to effective coordination and decision-making. Existing approaches, such as belief state estimation and inter-agent communication, often fall short. Belief-based methods are limited by their focus on past experiences without fully leveraging global information, while communication methods often lack a robust model to effectively utilize the auxiliary information they provide. To solve this issue, we propose Global State Diffusion Algorithm~(GlobeDiff) to infer the global state based on the local observations. By formulating the state inference process as a multi-modal diffusion process, GlobeDiff overcomes ambiguities in state estimation while simultaneously inferring the global state with high fidelity. We prove that the estimation error of GlobeDiff under both unimodal and multi-modal distributions can be bounded. Extensive experimental results demonstrate that GlobeDiff achieves superior performance and is capable of accurately inferring the global state.