Unveiling Decision Intention for Cooperative Multi-Agent Reinforcement Learning

Zeren Zhang, Zhiwei Xu, Guangchong Zhou, Dapeng Li, Bin Zhang, Guoliang Fan

Abstract: For cooperative multi-agent reinforcement learning, various methods have been proposed to enhance the collaborative strategy capabilities of agents. However, when agents make decisions, humans have no knowledge of their subsequent decision-making intentions or sub-goals. This lack of understanding hinders human comprehension of agent strategies and further research on agents. Currently, there are limited relevant studies. To address this problem, we propose a novel framework which can generate the decision intention of agents. We first formalize this problem and use states crucial to the task to express the decision intentions of agents. Then, we introduce the polarization index to measure the importance of states and select them for training. Finally, we learn the decision intentions through a diffusion model with rapid generation capability and generate them during the decision-making process. This study sheds light on the problem of agent decision intention and enhances the transparency of agent strategies, facilitating deeper research on agents. The experimental results demonstrate the effectiveness of our approach.