遗憾在人口网络游戏中的最小化:消失的异质性和趋同到平衡点
IEEE transactions on neural networks and learning systems
|October 30, 2025
概括
多种代理系统中的异质代理可以通过平滑的遗憾匹配来实现统一的行为. 这一过程减少了遗憾的差异,导致在不同的游戏环境中达成共识和趋同到平衡.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 计算经济学的计算经济学
背景情况:
- 预测大规模的多元代理行为是一个重大挑战.
- 代理的异质性可能会使平衡形成复杂化.
研究的目的:
- 调查异质性如何影响多代理系统中的平衡形成.
- 分析平滑的遗憾匹配在驱动不同的代理人走向统一行为的作用.
主要方法:
- 建模系统状态作为遗憾的概率分布.
- 使用连续性方程分析遗憾分布的演变.
主要成果:
- 顺的遗憾匹配减少了随着时间的推移而遗憾分布的差异.
- 异质性消失,导致代理商的共识.
- 在竞争和合作环境中证明了对量子反应平衡的趋同.
结论:
- 顺的遗憾匹配是在异质的多代理系统中实现共识的通用机制.
- 这项工作推进了多代理学习理论和平衡选择视角.
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