mlsanityData to 7 Oct 2026

Learning to Coordinate via Multiple Graph Neural Networks

arXiv 2104.03503 ↗First posted 8 Apr 2021cs.MA cs.AI cs.LGcited by 5

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Abstract (arXiv)

The collaboration between agents has gradually become an important topic in multi-agent systems. The key is how to efficiently solve the credit assignment problems. This paper introduces MGAN for collaborative multi-agent reinforcement learning, a new algorithm that combines graph convolutional networks and value-decomposition methods. MGAN learns the representation of agents from different perspectives through multiple graph networks, and realizes the proper allocation of attention between all agents. We show the amazing ability of the graph network in representation learning by visualizing the output of the graph network, and therefore improve interpretability for the actions of each agent in the multi-agent system.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 21 of its 24 references that are in the map. First posted on arXiv 8 Apr 2021.

Builds on

21 of 24 references.

References · 21

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9 more
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  9. 2017Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Gamescited by 93

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1; 2026: 1.

5 papers.

  1. 2023Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learningcited by 5
  2. 2022Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Networkcited by 3
  3. 2026Communication Methods in Multi-Agent Reinforcement Learningcited by 1
  4. 2023Mastering Complex Coordination through Attention-based Dynamic Graphcited by 0
  5. 2025Goal-Oriented Multi-Agent Reinforcement Learning for Decentralized Agent Teamscited by 0