{"type":"ScholarlyArticle","title":"MFVFD: A Multi-Agent Q-Learning Approach to Cooperative and Non-Cooperative Tasks","authors":[{"name":"Tianhao Zhang","slug":"tianhao-zhang","given":"Tianhao","family":"Zhang","affiliation":""},{"name":"Qiwei Ye","slug":"qiwei-ye","given":"Qiwei","family":"Ye","affiliation":""},{"name":"Jiang Bian","slug":"jiang-bian","given":"Jiang","family":"Bian","affiliation":""},{"name":"Guangming Xie","slug":"guangming-xie","given":"Guangming","family":"Xie","affiliation":""},{"name":"Tie-Yan Liu","slug":"tie-yan-liu","given":"Tie-Yan","family":"Liu","affiliation":""}],"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.","doi":"10.24963/ijcai.2021/70","url":"https://ijcai.org/proceedings/2021/70","pdf":"https://ijcai.org/proceedings/2021/0070.pdf","markdown":"https://ijcai.org/proceedings/2021/70.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/70","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":70,"pages":{"first":500,"last":506},"keywords":["Agent-based and Multi-agent Systems: Noncooperative Games","Agent-based and Multi-agent Systems: Multi-agent Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}