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.

基于多智能体强化学习的计算框架通过模拟囚徒困境游戏中的长期经验使智能体能够选择困境策略和互动邻居，并发现长期经验使智能体能够识别非合作邻居并偏好与合作邻居互动，进而导致具有相似策略的智能体聚集，增加网络互惠性和加强群体合作。