Proceedings

MFVFD: A Multi-Agent Q-Learning Approach to Cooperative and Non-Cooperative Tasks

Tianhao Zhang, Qiwei Ye, Jiang Bian, Guangming Xie, Tie-Yan Liu

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 500-506.

https://doi.org/10.24963/ijcai.2021/70

Keywords

  • Agent-based and Multi-agent Systems: Noncooperative Games
  • Agent-based and Multi-agent Systems: Multi-agent Learning

Abstract

Value function decomposition (VFD) methods under the popular paradigm of centralized training and decentralized execution (CTDE) have promoted multi-agent reinforcement learning progress. However, existing VFD methods proceed from a group's value function decomposition to only solve cooperative tasks. With the individual value function decomposition, we propose MFVFD, a novel multi-agent Q-learning approach for solving cooperative and non-cooperative tasks based on mean-field theory. Our analysis on the Hawk-Dove and Nonmonotonic Cooperation matrix games evaluate MFVFD's convergent solution. Empirical studies on the challenging mixed cooperative-competitive tasks where hundreds of agents coexist demonstrate that MFVFD significantly outperforms existing baselines.

Tianhao Zhang, Qiwei Ye, Jiang Bian, Guangming Xie, and Tie-Yan Liu. 2021. MFVFD: A Multi-Agent Q-Learning Approach to Cooperative and Non-Cooperative Tasks. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 500–506. https://doi.org/10.24963/ijcai.2021/70
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