数据驱动的H∞ 通过强化学习通过切换拓学的异质多代理系统的输出共识
IEEE transactions on cybernetics
|August 9, 2024
概括
一个新的强化学习算法使多代理系统能够跟踪领导者,尽管未知的动态和不断变化的连接. 这种方法使用适应观察者和政策梯度来进行强有力的控制,确保系统的稳定性.
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
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多代理系统面临着未知的动态,外部干扰和动态网络拓学的挑战.
- 由于切换拓学,由于缺少信息,Leader-following任务变得复杂.
研究的目的:
- 为离散时间异质多代理系统开发一种无模型的政策梯度增强学习算法.
- 解决系统中未知动态,外部干扰和切换拓的跟踪问题.
主要方法:
- 一个分布式适应观察者估计了领导者未知的动态和状态.
- 一个指数折扣值函数和离散时间游戏代数里卡蒂方程 (DTGAREs) 为控制策略推导.
- 一个基于数据的政策梯度算法在线近似DTGARE解决方案,避免了明确的系统知识.
主要成果:
- 拟议的算法成功地解决了离散时间异质多代理系统的跟踪问题.
- 使用离线数据集和体验重播可以提高数据利用效率.
- 通过探索指数折扣值的下限来确保系统的稳定性.
结论:
- 这种新的算法为复杂条件下的多代理追踪提供了强大而高效的解决方案.
- 无模型的强化学习与自适应观察者相结合,为分散控制提供了一种强大的方法.
- 模拟验证了拟议方法在确保系统稳定性和实现准确跟踪方面的有效性.
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