AMAC: Attention-based Multi-Agent Cooperation for Smart Load Balancing.

This paper proposes an Attention-based Multi-Agent Cooperation (AMAC) approach to reduce message exchange overhead in Multi-Agent Reinforcement Learning-based smart load balancing. AMAC shares only most relevant messages across agents to coordinate decision-making without degrading original performance. Experiments show that AMAC significantly lowers inter-agent communications overhead and learning complexity and outperforms multiple MARL benchmarks in Key Performance Indicators (KPIs) and Key Quality Indicators (KQIs).