Unveiling Decision Intention for Cooperative Multi-Agent Reinforcement Learning

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 This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), Y. Vorobeychik, S. Das, A. Nowé (eds.), May 19 – 23, 2025, Detroit, Michigan, USA. © 2025 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). agents. The experimental results demonstrate the effectiveness of our approach.

Authors

• Zeren Zhang The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences
• Zhiwei Xu Shandong University
• Guangchong Zhou The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences
• Dapeng Li The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences School of Artificial Intelligence, University of Chinese Academy of Sciences
• Bin Zhang Institute of Automation, CASIA & School of Artificial Intelligence, UCAS
• Guoliang Fan Institute of Automation, CASIA & School of Artificial Intelligence, UCAS

Keywords

• Diffusion model
• Multi-agent reinforcement learning
• Decision intention