加强学习驱动的适应性网络合作的发展.
Chunpeng Du1, Yikang Lu2, Haoran Meng3
1School of Mathematics, Kunming University, Kunming 650214, China.
Chaos (Woodbury, N.Y.)
|April 1, 2024
概括
代理人通过强化学习来调整他们的网络连接,以促进进化游戏中的合作. 这导致异质的,与现实世界类似的网络,并为生成无规模网络提供了一种新方法.
科学领域:
- 复杂的系统复杂的系统.
- 进化游戏理论 进化游戏理论
- 网络科学 网络科学
背景情况:
- 复杂的网络在各种现实世界的领域中普遍存在.
- 现实世界的网络往往自发地从代理互动中出现.
- 囚犯困境游戏 (PDG) 是研究合作的一个基本模型.
研究的目的:
- 研究适应性网络结构如何影响进化游戏中的合作.
- 探索强化学习在动态网络演变中的应用.
- 了解代理人修改其连接的网络的新兴属性.
主要方法:
- 设计了一种进化游戏模型,将囚犯困境游戏 (PDG) 纳入其中.
- 代理人利用强化学习自主调整他们的网络连接.
- 在稳定状态下分析了网络拓,度分布和模块化.
主要成果:
- 与同质网络相比,基于强化学习的适应性网络显著增强了合作.
- 网络拓学从同质状态演变为异质状态.
- 适应性网络展示了权力法度分布和社区结构,类似于现实世界的网络.
结论:
- 通过强化学习驱动的适应性网络进化是促进合作的可行机制.
- 这种方法为生成无规模网络提供了一种新的方法,与传统的增长模式不同.
- 这些发现为网络结构,合作的出现和复杂系统中的代理行为提供了新的见解.
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