Value-Aware Prediction Improves Robustness in Multi-Agent Coordination Under Communication Loss
A new method called Value-Aware MARO is proposed to enhance multi-agent coordination when communication between agents is unreliable. By weighting prediction loss with advantage estimates, the approach focuses learning on high-return behaviors, helping agents maintain performance even as communication failures increase. Experiments in simulated environments show that Value-Aware MARO prevents the performance collapse seen in standard methods under high communication dropout, achieving over 20% higher mean returns and a 64.7% reduction in variance compared to baselines.
Why it matters: This work offers a practical advance for multi-agent systems operating in real-world conditions where communication is often unreliable, improving robustness without extra hardware.
Full story at: arXiv Multiagent Systems ↗