The coordination between agents in multi-agent systems has become a popular topic in many fields. To catch the inner relationship between agents, the graph structure is combined with existing methods and improves the results. But in large-scale tasks with numerous agents, an overly complex graph would lead to a boost in computational cost and a decline in performance. Here we present DAGMIX, a novel graph-based value factorization method. Instead of a complete graph, DAGMIX generates a dynamic graph at each time step during training, on which it realizes a more interpretable and effective combining process through the attention mechanism. Experiments show that DAGMIX significantly outperforms previous SOTA methods in large-scale scenarios, as well as achieving promising results on other tasks.

翻译：多智能体系统中的智能体协调问题已成为多个领域的研究热点。为捕捉智能体间的内在关联，现有方法常引入图结构以提升性能。然而在含有大量智能体的大规模任务中，过于复杂的图结构会导致计算成本激增与性能下降。本文提出DAGMIX——一种新颖的基于图的价值分解方法。该方法摒弃全连接图，在训练过程中每个时间步动态构建图结构，并通过注意力机制实现更具可解释性与有效性的组合过程。实验表明，DAGMIX在大规模场景中显著优于现有最优方法，同时在其它任务上也取得了令人满意的结果。