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模糊的基于知识的等级增强学习,用于大型异质多代理系统
IEEE transactions on cybernetics
|December 31, 2025
概括
本研究介绍了多代理强化学习 (MARL) 的层次方法,该方法使用模糊逻辑来整合人类指导,提高可扩展性和异质性,即使在不确定的输入.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多代理强化学习 (MARL) 在平衡可扩展性和异质性方面面临挑战,特别是随着不确定性增加.
- 结合密集的本地和稀疏的全球相互作用,可以提高MARL的可扩展性并保持代理异质性.
- 人类的社会行为为设计更有效的MARL系统提供了洞察力.
研究的目的:
- 提出一种新的等级方法,将人类指导整合到多代理系统 (MAS) 中.
- 利用抽象知识从人类转移,使用模糊逻辑来管理不确定性并减少人类的努力.
- 通过结构化的人类指导方法来增强MARL的可扩展性和异质性.
主要方法:
- 开发了一种分层方法,结合了个人行动指导和代理关系的注意力图.
- 采用模糊逻辑来管理人类指导中的不确定性,使抽象知识转移成为可能.
- 设计了一种与各种 MARL 算法兼容的端到端方法.
主要成果:
- 在StarCraft多代理挑战 (SMAC) 和SMACv2环境中的实证评估证明了该方法的有效性.
- 该方法在可扩展性和异质性方面显示出显著的改进.
- 这种方法即使在低性能,模糊的人类指导下也被证明是有效的.
结论:
- 提出的等级方法成功地将人类指导与MARL系统相结合.
- 模糊逻辑有效地管理人类指导的不确定性,提高MARL的性能.
- 这种方法为开发更强大,更可扩展的 MARL 解决方案提供了一个有希望的方向.
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