基于观察者的人类在循环中的最佳输出集群对多代理系统的同步控制:一种无模型的强化学习方法
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究引入了非线性多代理系统 (MAS) 的基于观察者的循环中人 (HiTL) 控制. 它通过强化学习实现了最佳的输出集群同步,即使是未知的领导输入.
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) 存在复杂的控制挑战,特别是在实现同步行为方面.
- 人在循环 (HiTL) 控制整合了人类操作员,以加强系统管理.
- 当系统状态或输入不能直接测量时,基于观察者的控制至关重要.
研究的目的:
- 为了研究基于观察者的人在循环 (HiTL) 对非线性MAS的最佳输出集群同步控制.
- 开发一种控制策略,以适应具有未知,时间变化的输入的非自主领导者.
- 使追随者代理能够与领导者的输出实现同步,而无需直接访问它.
主要方法:
- 设计一个非自主领导者与人类监控的输入.
- 对于领导者不可用的输出,开发一个规定的时间的收观察者.
- 增强系统的构建和成本函数的制定.
- 应用非政策强化学习来解决汉密尔顿-雅可比安-贝尔曼方程 (HJBE).
- 使用单个关键神经网络 (NN) 训练的最小平方法实现.
主要成果:
- 设计的观察者在实际规定的时间内实现了趋同.
- 政策之外的强化学习算法成功地学习了HJBE解决方案,而没有完整的系统知识.
- 单一的批评NN方法有效地减轻了计算负担.
- 模拟结果验证了拟议的HiTL最佳输出集群同步控制方案的有效性.
结论:
- 开发的基于观察者的HiTL控制策略对非线性MAS有效.
- 强化学习提供了一种可行的方法,以部分系统信息解决复杂的控制问题.
- 该方法在多代理情景中确保了强大的和高效的集群同步.
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