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Updated: Jan 17, 2026

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联合学习适应性动态编程大规模的多代理人中场游戏基于最佳共识的最佳共识
IEEE transactions on cybernetics
|September 22, 2025
概括
本研究介绍了大型多代理系统的联合学习自适应动态编程 (FL-ADP) 控制方案. 这种新的方法通过使用平均场游戏 (MFGs) 接近代理相互作用来确保稳定的最佳共识控制.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 分布式系统 分布式系统
背景情况:
- 由于众多的相互作用和冲突,大规模的多代理系统面临着实现实时最佳共识控制的挑战.
- 现有的方法难以应对这些系统的复杂性和规模,需要先进的控制策略.
研究的目的:
- 开发一种新的联合学习自适应动态编程 (FL-ADP) 控制方案,以在大型多代理系统中实现最佳共识.
- 为应对大规模代理人互动和利益冲突所带来的挑战.
- 解决基于平均场游戏 (MFGs) 的最佳共识问题.
主要方法:
- 使用平均场游戏 (MFGs) 估计单个药剂相互作用.
- 开发一种新的非折扣性绩效指数函数,包括平均场合和跟踪错误.
- 利用一个批评质量神经网络来解决合的汉密尔顿 - 雅各比 - 贝尔曼和福克 - 普朗克 - 科尔摩戈罗夫方程.
- 制定一个由事件触发的联合学习机制,以实现算法融合和通信效率.
主要成果:
- 推导一个近似的最佳控制政策和量化集体行为概率密度.
- 使用莱普诺夫直接方法,保证追踪错误和重量估计错误的统一终极边界性.
- 验证FL-ADP计划的有效性和合理性,通过对大型多重无人机空中飞行器系统的模拟.
结论:
- 拟议的FL-ADP控制方案有效地解决了大规模多代理系统中的最佳共识问题.
- 该方法平衡了通信资源消耗与算法融合,优于现有方法.
- 开发的技术为大规模系统的实时自适应性最佳共识控制提供了强大而高效的解决方案.
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