一个政策共振方法来解决责任扩散的问题在多代理强化学习的多代理强化学习
IEEE transactions on neural networks and learning systems
|August 1, 2024
概括
最先进的多代理强化学习算法存在责任扩散 (RD),阻碍了协作. 一种新的政策共振 (PR) 方法修改了联合政策,以改善在复杂任务中的代理合作.
科学领域:
- 人工智能的人工智能
- 多代理系统 多代理系统
- 强化学习是一种强化学习.
背景情况:
- 当前的多代理强化学习 (MARL) 算法经常继承单代理探索策略.
- 这种遗传导致了由于"责任扩散" (RD) 的合作失败,其中代理人避免了少数角色.
研究的目的:
- 从理论上分析MARL的R&D的原因,将其与勘探-开发困境联系起来.
- 提出一种新的方法,政策共 (PR),以减轻R&D和加强MARL的合作探索.
主要方法:
- 在大型MARL系统中对勘探-开采困境的理论分析.
- 制定政策共 (PR) 方法,以重构联合代理政策,同时保持个人政策.
- 与最先进的MARL算法集成的PR的经验评估.
主要成果:
- 证明拟议的政策共 (PR) 方法有效地解决了责任扩散 (RD) 问题.
- 在使用PR时,在复杂的合作任务中,代理人的协作表现显著改善.
- 在多个基准任务中验证公关的有效性.
结论:
- 政策共振 (PR) 方法提供了一个可行的解决方案,以加强多代理强化学习的协作.
- PR成功地修改了协作探索策略,克服了单个代理继承的局限性.
- 这项工作为复杂的合作任务提供了更强大和更有效的MARL系统的途径.
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