MFVFD: A Multi-Agent Q-Learning Approach to Cooperative and Non-Cooperative Tasks
Tianhao Zhang, Qiwei Ye, Jiang Bian, Guangming Xie, Tie-Yan Liu
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.
BibTeX
@inproceedings{ijcai2021p70,
title = {MFVFD: A Multi-Agent Q-Learning Approach to Cooperative and Non-Cooperative Tasks},
author = {Zhang, Tianhao and Ye, Qiwei and Bian, Jiang and Xie, Guangming and Liu, Tie-Yan},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {500--506},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/70},
url = {https://doi.org/10.24963/ijcai.2021/70},
}