用门控图网络加速多智能体动态平均估计，收敛更快更稳。
A Gated Graph Neural Network Approach to Fast-Convergent Dynamic Average Estimation
- 基于门控图网络建模分布式自回归过程，实现快速收敛。
- 实验显示收敛速度和精度显著优于传统方法。
- 适合对实时性要求高的多智能体协同系统应用。
动态平均估计是多智能体系统中的关键问题，使各智能体仅通过局部信息交换即可协同估计时变信号。传统模型方法常面临收敛速度慢和对网络拓扑变化敏感的问题。本文提出一种基于门控图神经网络（GGNN）的新型学习方法，实现完全分布式、快速收敛的动态平均估计。利用GGNN的固有结构，将估计过程建模为分布式自回归，确保快速收敛与稳定性。训练中引入正则项以保证收敛性，并设计编码-解码机制，在不牺牲精度的前提下降低通信开销。大量数值实验表明，该方法在收敛速度和估计精度上均显著优于传统模型基估计器，是多智能体动态平均估计的有力替代方案。
原文摘要 · Abstract (English)
Dynamic average estimation is a critical problem in multi-agent systems, enabling agents to collaboratively estimate time-varying signals using only local information exchange. Traditional model-based approaches often face challenges related to convergence speed and sensitivity to network topology changes. This paper introduces a novel learning-based solution leveraging Gated Graph Neural Networks (GGNNs) for fast-convergent dynamic average estimation in a fully distributed manner. Taking advantage of the inherent structure of GGNNs, the proposed method models the estimation process as a distributed autoregressor, ensuring rapid convergence while maintaining stability. We incorporate a regularization term during training to enforce convergence guarantees and introduce an encoding-decoding mechanism to reduce communication overhead without sacrificing accuracy compared to standard GGNNs. Extensive numerical experiments demonstrate that our approach significantly outperforms conventional model-based estimators in terms of both convergence speed and precision, making it a promising alternative for multi-agent applications that require dynamic average estimation.
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