提出GlobeDiff算法，用扩散模型从局部观测推断全局状态。
GlobeDiff: State Diffusion Process for Partial Observability in Multi-Agent Systems
- 将全局状态推断建模为多模态扩散过程，解决局部观测模糊问题。
- 理论证明在单峰与多峰分布下估计误差可被控制。
- 在多智能体系统中显著提升状态推断精度，适合复杂协作场景。
在多智能体系统中，局部可观测性是有效协同与决策的关键障碍。现有方法如信念状态估计和智能体间通信常存在局限：基于信念的方法仅依赖历史经验，未能充分利用全局信息；通信方法虽能传递辅助信息，却缺乏有效利用机制。为此，本文提出全局状态扩散算法（GlobeDiff），基于局部观测推断全局状态。通过将状态推断过程建模为多模态扩散过程，GlobeDiff克服了状态估计中的不确定性，同时实现高保真度的全局状态重构。理论证明，在单峰与多峰分布下，GlobeDiff的估计误差均可被界定。大量实验表明，该方法性能优越，能准确推断全局状态。
原文摘要 · Abstract (English)
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.
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