相关实验视频
Updated: Sep 19, 2025

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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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人口游戏中的异质性,强化学习和混乱
Jakub Bielawski1, Thiparat Chotibut2, Fryderyk Falniowski1
1Department of Mathematics, Krakow University of Economics, Kraków 31-510, Poland.
概括
拥堵游戏中的多代理增强学习动态可以随着不同的代理学习率变得混乱,增加社会成本. 然而,平均行为仍然趋于稳定的纳什平衡.
科学领域:
- 游戏理论 游戏理论
- 机器学习 机器学习
- 动态系统 动态系统
背景情况:
- 拥堵游戏是一类具有已知的纳什平衡的潜在游戏.
- 这些游戏中的代理人个人影响微不足道,激励措施一致.
研究的目的:
- 在具有异质学习率的拥堵游戏中研究多代理强化学习 (MARL) 动态.
- 分析纳什平衡的偏差以及由此产生的社会成本.
- 探索微观动力学与宏观结果之间的联系.
主要方法:
- 在非原子拥堵游戏中研究离散时间MARL动态.
- 采用动态系统技术来分析代理适应和人口水平的结果.
- 检查了各种代理人的信仰和不同学习率的场景.
主要成果:
- 具有异质学习速率的MARL动态可以表现出不稳定和混乱的行为,偏离静态的纳什平衡.
- 这些混乱的政权导致社会成本增加.
- 时间平均的宏观行为汇聚到精确的纳什平衡,尽管微观复杂性.
结论:
- 在MARL中,异质的学习速度可以在拥堵游戏中引入复杂的动态.
- 混乱的微观动力学和稳定的宏观平衡行为之间存在联系.
- 这些发现为在离散时间内研究复杂的学习动态铺平了道路.
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