A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication

📰 ArXiv cs.AI

Learn how multi-agent deep reinforcement learning uses graph neural network-based communication to improve coordination and convergence

advanced Published 30 Apr 2026

Action Steps

Read the survey to understand the current state of multi-agent deep reinforcement learning with graph neural network-based communication
Implement a graph neural network to enable communication between agents in a multi-agent reinforcement learning environment
Evaluate the performance of the graph neural network-based communication mechanism using metrics such as convergence rate and coordination efficiency
Compare the results with other communication mechanisms to determine the effectiveness of graph neural networks
Apply the graph neural network-based communication mechanism to a real-world multi-agent system to improve its performance

Who Needs to Know This

Researchers and engineers working on multi-agent systems and deep reinforcement learning can benefit from this survey to improve their understanding of graph neural network-based communication

Key Insight

💡 Graph neural networks can be used to learn communication mechanisms in multi-agent reinforcement learning, improving coordination and convergence