学习角色与新兴的社会价值导向
IEEE transactions on pattern analysis and machine intelligence
|October 13, 2025
概括
本研究引入了一个新的框架,即与新兴SVO学习角色 (RESVO),通过使代理人能够开发社会价值导向 (SVO) 和有效合作来解决社会困境.
科学领域:
- 人工智能的人工智能
- 多代理系统 多代理系统
- 社会科学 社会科学 社会科学
背景情况:
- 当个人的理性选择导致集体不理性的结果时,社会困境就会出现.
- 多代理强化学习 (MARL) 利用社会科学概念,如社会价值导向 (SVO) 来应对合作任务中的这些挑战.
研究的目的:
- 提出一种新的学习框架,即学习角色与新兴SVO (RESVO),用于跨时代社会困境 (ISD).
- 将角色学习转化为SVO的出现,通过利他主义奖励共享促进合作.
主要方法:
- 从人类社会引入"分工"机制到MARL.
- 开发了RESVO框架,将政策与基于SVO的嵌入空间中的角色联系起来.
- 利用一种新的等级调节器和相互信息最大化器来调节政策.
主要成果:
- 雷斯沃成功地使代理人能够学习角色并开发SVO.
- 在不同复杂度的ISD中表现出稳定的分工和合作.
- 展示了利他主义在合作完成任务的奖励分享中的有效性.
结论:
- 通过将角色出现与SVO整合起来,RESVO为时间间的社会困境提供了一个有希望的解决方案.
- 该框架在多代理系统中促进了稳定的合作和分工.
- 这种方法突出了社会科学原则在推进MARL研究方面的潜力.
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