线性离散时间多代理系统的多步式基于Q学习的最佳共识控制
IEEE transactions on cybernetics
|June 24, 2025
概括
本研究介绍了多代理多步Q学习 (MaMsQL) 以在多代理系统中实现最佳的共识控制. 该方法有效地平衡了勘探和开采,在模拟中表现优于单步方法.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 多代理系统 多代理系统
背景情况:
- 多代理系统需要强大的控制策略来管理复杂的相互作用和环境不确定性.
- 在动态环境中,平衡勘探和开发对于高效的学习至关重要.
研究的目的:
- 为多代理系统开发最佳的共识控制方法.
- 通过解决复杂的动态和不确定性来提高学习效率.
- 在代理相互作用中实现勘探和利用之间的平衡.
主要方法:
- 开发多代理多步Q学习 (MaMsQL) 算法.
- 制定Q函数以建立最佳Q函数的纳什平衡.
- 对MaMsQL方法的理论趋同证明.
- 使用专门的Actor-Critic网络架构实现.
主要成果:
- Q函数公式证明了最佳的Q函数构成了纳什平衡.
- 该MaMsQL方法证明了趋同,确保稳定性和有效性.
- 模拟证实了MaMsQL对多代理单步Q学习的优势.
结论:
- MaMsQL提供了一种有效的解决方案,用于在多代理系统中实现最佳的共识控制.
- 该方法成功地处理了复杂的代理相互作用和环境不确定性.
- 行动者-关键的实施验证了MaMsQL.的实际应用性和增强的性能.
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