# MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2012.09762 · HTML: https://mlsanity.com/paper/2012.09762/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Towards a Standardised Performance Evaluation Protocol for Cooperative MARL](https://mlsanity.com/paper/2209.10485/) (2022)
- [Joint Attention for Multi-Agent Coordination and Social Learning](https://mlsanity.com/paper/2104.07750/) (2021)
- [Graph-Enhanced Policy Optimization in LLM Agent Training](https://mlsanity.com/paper/2510.26270/) (2025)
- [Mastering Complex Coordination through Attention-based Dynamic Graph](https://mlsanity.com/paper/2312.04245/) (2023)

## Abstract (arXiv)

Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains. In this paper, we propose a novel approach, called MAGNet, to multi-agent reinforcement learning that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique. We applied our MAGnet approach to the synthetic predator-prey multi-agent environment and the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including Multi-agent Deep Q-Networks (MADQN), Multi-agent Deep Deterministic Policy Gradient (MADDPG), and QMIX