Abstract: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.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2604.25972 [cs.LG] |
| (or arXiv:2604.25972v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.25972 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Rencontres des Jeunes Chercheurs en Intelligence Artificielle (RJCIA), Plate-Forme Intelligence Artificielle (PFIA), Jun 2026, Arras, France |
Submission history
From: Valentin Cuzin-Rambaud [view email] [via CCSD proxy]
[v1]
Tue, 28 Apr 2026 07:50:24 UTC (106 KB)