{"type":"ScholarlyArticle","title":"Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning","authors":[{"name":"Tianyu Ren","slug":"tianyu-ren","given":"Tianyu","family":"Ren","affiliation":"University of Manchester"},{"name":"Xiao-Jun Zeng","slug":"xiao-jun-zeng","given":"Xiao-Jun","family":"Zeng","affiliation":"University of Manchester"}],"abstract":"The significance of network structures in promoting group cooperation within social dilemmas has been widely recognized. Prior studies attribute this facilitation to the assortment of strategies driven by spatial interactions. Although reinforcement learning has been employed to investigate the impact of dynamic interaction on the evolution of cooperation, there remains a lack of understanding about how agents develop neighbour selection behaviours and the formation of strategic assortment within an explicit interaction structure. To address this, our study introduces a computational framework based on multi-agent reinforcement learning in the spatial Prisoner's Dilemma game. This framework allows agents to select dilemma strategies and interacting neighbours based on their long-term experiences, differing from existing research that relies on preset social norms or external incentives. By modelling each agent using two distinct Q-networks, we disentangle the coevolutionary dynamics between cooperation and interaction. The results indicate that long-term experience enables agents to develop the ability to identify non-cooperative neighbours and exhibit a preference for interaction with cooperative ones. This emergent self-organizing behaviour leads to the clustering of agents with similar strategies, thereby increasing network reciprocity and enhancing group cooperation.","doi":"10.24963/ijcai.2024/22","url":"https://ijcai.org/proceedings/2024/22","pdf":"https://ijcai.org/proceedings/2024/0022.pdf","markdown":"https://ijcai.org/proceedings/2024/22.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/22","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":22,"pages":{"first":193,"last":201},"keywords":["Multidisciplinary Topics and Applications: MTA: Social sciences","Game Theory and Economic Paradigms: GTEP: Cooperative games","Agent-based and Multi-agent Systems: MAS: Coordination and cooperation","Machine Learning: ML: Multiagent Reinforcement Learning"],"videos":[],"references":[{"position":1,"raw":"[Abeywickrama et al., 2023] Dhaminda B Abeywickrama, Nathan Griffiths, Zhou Xu, and Alex Mouzakitis. 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