Recent work in the multi-agent domain has shown the promise of Graph Neural Networks (GNNs) to learn complex coordination strategies. However, most current approaches use minor variants of a Graph Convolutional Network (GCN), which applies a convolution to the communication graph formed by the multi-agent system. In this paper, we investigate whether the performance and generalization of GCNs can be improved upon. We introduce ModGNN, a decentralized framework which serves as a generalization of GCNs, providing more flexibility. To test our hypothesis, we evaluate an implementation of ModGNN against several baselines in the multi-agent flocking problem. We perform an ablation analysis to show that the most important component of our framework is one that does not exist in a GCN. By varying the number of agents, we also demonstrate that an application-agnostic implementation of ModGNN possesses an improved ability to generalize to new environments.

翻译：多试剂领域最近的工作表明,图形神经网络(GNN)有望学习复杂的协调战略,但是,大多数目前的做法都采用图形革命网络(GCN)的微小变体,对由多试剂系统形成的通信图进行演化。在本文中,我们调查GCN的性能和普及性是否可以改进。我们引入了ModGNN,这是一个分散化的框架,作为GCN的概括性,提供了更大的灵活性。为了检验我们的假设,我们根据多试剂聚集问题的若干基线评估了ModGNN的落实情况。我们进行了调整分析,以表明我们框架的最重要组成部分是GCN所没有的。我们通过改变物剂的数量,还表明ModGNN的应用程序化实施提高了向新环境推广的能力。