合作的演变以模仿学习和强化学习的共存为指导
Wei Tang1,2, Guoling Wang3, Zhiyan Xing2
1School of Information Engineering, Guizhou Open University, Guiyang, Guizhou, 550023, China.
Scientific reports
|July 18, 2025
概括
本研究引入了混合更新规则,将模仿学习和强化学习结合起来,以促进合作. 这种新的方法有效地促进了各种游戏理论场景之间的合作,优于单一策略方法.
科学领域:
- 进化游戏理论 进化游戏理论
- 计算社会科学 计算社会科学
- 人工智能的人工智能
背景情况:
- 促进合作是自然科学和社会动态的持续挑战.
- 现有的研究往往侧重于单一的战略更新规则,忽视了现实世界的复杂性.
- 个人经常采用多种策略来应对动态环境.
研究的目的:
- 引入和评估一种新的混合更新规则,该规则结合了模仿学习和强化学习.
- 调查这种混合策略在促进不同游戏理论模型之间的合作方面的有效性.
- 为了比较混合更新规则与单一战略方法的性能.
主要方法:
- 制定混合更新规则,整合模仿学习 (采用成功同行策略) 和强化学习 (从个人经验中学习).
- 模拟囚犯困境游戏 (PDG),共存游戏 (CG) 和协调游戏 (CoG).
- 在混合群体和结构化网络拓 (方格格子) 中实施模拟.
主要成果:
- 在囚犯困境游戏中,混合规则导致合作和叛逃的共存,减轻了普遍的叛逃问题.
- 合作水平超过了共存游戏中的混合纳什平衡.
- 在协调游戏中观察到合作者的优势.
- 在所有模拟游戏中,混合更新规则在促进合作方面表现优异,而单一战略更新规则则则相比.
结论:
- 拟议的混合更新规则,结合模仿和强化学习,在促进合作方面非常有效.
- 这种方法为理解在动态环境中的合作提供了一个更现实的,更成功的模型.
- 这些发现强调了结合学习策略应对复杂的社会困境的潜力.
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