在切换拓学下,多代理系统的输出共识的双层增强学习
IEEE transactions on cybernetics
|April 10, 2024
概括
本研究介绍了一种新的双层增强学习算法,用于在具有切换拓的离散时间多代理系统中实现基于数据的输出共识. 该方法可以确保系统稳定性和融合,而无需先前了解系统动态.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 网络化系统 网络化系统
背景情况:
- 在多代理系统中达成共识对于协调行为至关重要.
- 在多代理系统中切换拓对传统的控制算法构成挑战,原因是系统动态的时间变化.
- 现有的强化学习方法与切换拓学的内在时间变化的内核矩阵作斗争.
研究的目的:
- 开发基于数据的强化学习算法,以在离散时间的多代理系统中实现输出共识,并采用切换拓.
- 在处理切换变化的内核矩阵时,克服现有算法的局限性.
- 提出适用于固定和切换拓的分布式控制政策.
主要方法:
- 提出了一种新的两层增强学习算法来处理切换变化的内核矩阵.
- 一个基于数据的分布式控制政策被设计用于实施.
- 对拟议的算法进行了收分析.
主要成果:
- 拟议的算法有效地在切换拓下实现基于数据的输出共识.
- 开发的控制政策适用于固定和开关网络拓.
- 该方法消除了先前工作中发现的对领导者动态矩阵固有值的限制性假设.
结论:
- 这种新的双层强化学习方法成功地解决了在具有切换拓的离散时间多代理系统中输出共识的挑战.
- 基于数据的分布式控制政策为现实世界的应用提供了实际的解决方案.
- 模拟示例验证了算法的有效性和融合性质.
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