从观察和干预数据的模型预测复杂系统控制
Muyun Mou1,2, Yu Guo3, Fanming Luo2
1School of Systems Science, Beijing Normal University, Beijing 100875, China.
Chaos (Woodbury, N.Y.)
|September 19, 2024
概括
我们开发了一个新的框架来控制复杂的系统与有限的干预. 该方法使用观察数据进行预训练和模型预测控制,以微调,降低成本和改善概括性.
科学领域:
- 复杂系统科学 复杂系统科学
- 控制理论 控制理论
- 机器学习 机器学习
背景情况:
- 复杂系统表现出新出现的行为,使得数据驱动的建模和控制至关重要.
- 传统的控制方法在高干预成本和有限的干预数据方面扎.
- 通常可获得大量的观测数据,但直接干预成本昂贵.
研究的目的:
- 开发一种新的框架,以最小的在线干预来控制复杂系统.
- 在复杂系统中的高维状态行动空间中应对挑战.
- 利用丰富的观测数据进行有效的系统控制.
主要方法:
- 引入了一个两阶段模型预测复杂系统控制框架.
- 采用线下预培训,使用观测数据来建模系统动态.
- 利用在线微调与干预模型预测控制的变体进行干预.
- 开发了动作扩展图形神经网络,以建模马尔科夫决策过程.
- 设计了一个层次化的行动空间,以有效地学习干预策略.
主要成果:
- 拟议的框架在Boids,Kuramoto和SIS超人口环境中表现出强的表现.
- 实现了加速融合和强大的泛化能力.
- 与基线算法相比,显著降低干预成本.
- 在复杂的系统中有效处理高维状态动作空间.
结论:
- 两阶段框架为控制具有有限干预数据的复杂系统提供了有效的解决方案.
- 动作扩展图形神经网络和层次动作空间是解决这个问题的关键创新.
- 这种方法对需要高效复杂系统控制的现实应用具有前景.
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