通过在神经进化和强化学习方面判断一个小组的表现,通过其最弱的成员来鼓励合作
Jory Schossau1, Bamshad Shirmohammadi2, Arend Hintze1,2
1BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI, United States.
Frontiers in robotics and AI
|August 11, 2025
概括
根据他们最弱的成员奖励自主代理人促进公平,提高集团绩效. 这种最弱环的策略鼓励公平的结果,有利于集体和个人代理人.
科学领域:
- 人工智能的人工智能
- 多代理系统 多代理系统
- 行为经济学是一种行为经济学.
背景情况:
- 自主代理人越来越多地被部署在社会领域,需要制定决策策略,以平衡个人目标和群体福利.
- 个人贪的策略可能导致小组表现不佳,如交通拥堵和网络故障.
- 需要一种方法来促进自主代理人之间的公平结果.
研究的目的:
- 为自主代理人引入一种新的奖励系统,优先考虑群体公平性.
- 调查"最弱环"奖励指标对进化和强化学习框架内的代理行为的影响.
- 展示如何将代理人的激励与最弱势成员的表现相协调,可以提高集体结果.
主要方法:
- 在进化和强化学习中实施了"最弱环"奖励系统.
- 代理人的奖励明确基于小组中最不成功的成员的表现.
- 将结果与个别优化的奖励策略进行比较.
主要成果:
- 最弱成员奖励系统有效地促进了自主代理人之间的公平行为.
- 使用该系统的代理人平衡了集体利益与个人表现,从而导致更公平的集体结果.
- 与自私的策略相比,这种方法提高了整体团队的效率,稳定性和个体代理人的成功.
结论:
- 最弱环的奖励框架成功地鼓励自主代理人的公平和合作行为.
- 这种方法提供了一个可扩展的方法来优化多代理系统的公平性和性能.
- 研究结果表明,以集体福利为重点的激励结构可以改善复杂社会领域的结果.
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