通过强化学习促进自愿囚犯困境游戏中的合作
1School of Information Technology and Artificial Intelligence, Zhejiang University of Finance & Economics, Hangzhou 310018, China.
Chaos (Woodbury, N.Y.)
|April 15, 2025
概括
这项研究通过在Q学习中引入第三种策略和社会回报来加强游戏理论中的合作. 增加的社会关注促进了合作和平均回报,超越了个人利益.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 计算社会科学 计算社会科学
背景情况:
- 传统的游戏理论模型经常使用简化的两种策略框架 (合作与叛逃).
- 这些有限的模型未能捕捉到现实世界合作场景的细微动态.
- 强化学习在促进合作方面表现有前途,但需要更复杂的框架.
研究的目的:
- 扩展"囚犯困境"游戏,加入第三种策略:"单独行动".
- 通过使用Q学习,研究社会回报和社会关注对合作动态的影响.
- 分析在多战略环境中推动加强合作的机制.
主要方法:
- 集成Q学习,一个强化学习算法,进入囚犯困境框架.
- 引入了三种不同的策略:合作,叛逃和"独自行动".
- 根据合作邻居的数量定义了代理状态,并将社会回报纳入Q表更新.
主要成果:
- 数字模拟表明,合作水平显著增加,社会关注度增加.
- 随着社会关注的增加,代理人的平均报酬也得到了改善.
- 社会回报被确定为一个关键因素,使代理商能够考虑集体利益而不是个人收益.
结论:
- 拟议的Q学习框架有三个策略和社会回报,有效地促进合作.
- 增加社会关注和考虑社会利益对于增强合作行为至关重要.
- 这种模型提供了一个更现实的代表合作动态在复杂的社会系统.
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