Communication-efficient Multi-Agent Reinforcement Learning with Spatiotemporal Information Hub

Abstract

Centralized training with decentralized execution (CTDE) is a framework for MARL with wide applications. In the CTDE paradigm, agents leverage global state information during training to mitigate the non-stationarity of the MARL environment, but must rely solely on partial observations during execution. Recent work has highlighted the growing importance of inter-agent communication for more effective learning and coordination. However, most existing methods overlook the fact that real-world communication channels are often bandwidth-constrained and imperfectly reliable. Toward more communication-efficient and robust MARL, we extend the conventional CTDE framework with an information hub. The hub collects local observations from the agents to restore the global state, which is then delivered to the agents on demand. To this end, technical mechanisms are designed to enable effective global reconstruction with incomplete observations, as well as agent-specific attention to the reconstructed global information. Experiments on multiple cooperative MARL benchmarks demonstrate that our method achieves state-of-the-art performance compared to popular MARL algorithms while substantially reducing communication overhead and exhibiting strong robustness under imperfect communication channels.