In multi-agent reinforcement learning (MARL), the integration of a communication mechanism, allowing agents to better learn to coordinate their actions and converge on their objectives by sharing information. Based on an interaction graph, a subclass of methods employs graph neural networks (GNNs) to learn the communication, enabling agents to improve their internal representations by enriching them with information exchanged. With growing research, we note a lack of explicit structure and framework to distinguish and classify MARL approaches with communication based on GNNs. Thus, this paper surveys recent works in this field. We propose a generalized GNN-based communication process with the goal of making the underlying concepts behind the methods more obvious and accessible.
@article{arxiv.2604.25972,
title = {A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication},
author = {Valentin Cuzin-Rambaud and Laetitia Matignon and Maxime Morge},
journal= {arXiv preprint arXiv:2604.25972},
year = {2026}
}