arXiv cs.MA· Yefei Zhang, Yuxuan Zhao·· 9 天前AI 评分22
公共品困境中多智能体学习如何形成空间模式
Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas
AI 导读
固定数量的合作者与背叛者用表格型 Q-learning 和局部观测独立学习移动策略，合作者学习会在资源峰值周围形成聚集，共同适应则改变聚集强度与移动方式。在固定训练预算下，合作者高学习率、背叛者低学习率时福利损失最大，部分条件下还会出现由共同方向偏好支撑的移动带。在测试的合作者学习条件下，平均集体福利低于随机移动，而对智能体施加拥挤成本可挽回大部分福利损失。
正文 · 原文
Abstract:Spatial public goods models show that prescribed movement toward richer locations can generate spatial patterns. We ask how such patterns emerge when agents learn where to move and how learning rates shape their consequences for collective welfare. Fixed populations of cooperators and defectors independently learn movement policies using tabular Q-learning and local observations. Cooperator learning generates clusters around resource peaks, while co-adaptation changes their strength and motion. At a fixed training budget, the largest welfare losses occur when cooperators learn at high rates and defectors at low rates. In part of this regime, learned policies also generate traveling bands supported by a shared directional preference. The conditions supporting travel change with further training, so these patterns reflect training history rather than an established asymptotic outcome. Across the tested learning-rate conditions with cooperator learning, mean collective welfare falls below random movement because increased crowding outweighs gains in resource benefit. Charging agents for the crowding they impose on others during learning recovers much of the welfare loss in the tested conditions. These results connect learning rates to the emergence and welfare costs of spatial organization driven by individual rewards.
| Subjects: | Multiagent Systems (cs.MA); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.12321 [cs.MA] |
| (or arXiv:2610.12321v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12321 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yefei Zhang [view email]
[v1]
Thu, 8 Oct 2026 17:03:35 UTC (847 KB)
来源：arXiv cs.MA · arxiv.org