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基于经验的综合强化学习共识,用于未知的多代理系统
Longquan Ma1, Huarong Zhao2, Yuhao Chen1
1Engineering Research Center of Internet of Things Applications Ministry of Education, Jiangnan University, Wuxi, 214122, Jiangsu, China.
Scientific reports
|September 26, 2025
概括
本研究介绍了非线性多代理系统的综合强化学习算法,使得最优的共识控制,而不需要识别系统动态. 该方法确保了稳定的学习,并避免了改善性能的局部最佳值.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多代理系统 (MAS) 面临复杂的控制挑战,特别是未知的动态.
- 在MAS中达成共识 (协议) 对于协调任务至关重要.
- 传统的政策代方法通常需要系统模型识别.
研究的目的:
- 为具有未知动态的非线性MAS开发最佳的共识控制策略.
- 使用在线整体强化学习实现政策代算法.
- 解决和克服在线学习中的本地最佳的挑战.
主要方法:
- 一个批评者-演员神经网络架构被整合到政策代中.
- 在线综合强化学习被用来处理未知的系统动态.
- 基于经验的重量调节法被引入,以确保持续的兴奋.
主要成果:
- 拟议的算法成功实现了最佳的共识控制.
- 该系统表现出了不对称的稳定性.
- 神经网络的重量在学习过程中趋同.
- 模拟研究验证了算法的有效性和正确性.
结论:
- 批评者-演员神经网络方法有效地绕过了MAS控制中的动态识别需求.
- 开发的算法为非线性系统的最佳共识提供了强大的解决方案.
- 这些发现有助于多代理系统智能控制的进步.
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