Mastering Complex Coordination through Attention-based Dynamic Graph

• URL: http://arxiv.org/abs/2312.04245v1
• Date: Thu, 7 Dec 2023 12:02:14 GMT
• Title: Mastering Complex Coordination through Attention-based Dynamic Graph
• Authors: Guangchong Zhou, Zhiwei Xu, Zeren Zhang and Guoliang Fan
• Abstract summary: 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. Experiments show that DAGmix significantly outperforms previous SOTA methods in large-scale scenarios.
• Score: 14.855793715829954
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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