强化操作员学习 (ROL):一个混合的DeepONet引导的强化学习框架,用于稳定Kuramoto-Sivashinsky方程
Nadim Ahmed1, Md Ashraful Babu1, Muhammad Sajjad Hossain2
1Department of Physical Sciences, Independent University, Bangladesh, Dhaka, Bangladesh.
PloS one
|January 30, 2026
概括
增强操作员学习 (ROL) 是一种混合控制方法,可显著稳定混乱系统. 这种方法结合了深度运营商网络和双延迟深度决定性政策梯度,以获得卓越的性能和效率.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 应用数学 应用数学 应用数学
背景情况:
- 复杂的系统经常表现出混乱的行为,使得它们难以控制.
- 传统的控制方法与高维,非线性动力学作斗争.
研究的目的:
- 引入强化操作员学习 (ROL),一种新的混合控制范式.
- 通过Kuramoto-Sivashinsky方程来证明ROL在稳定时空混乱中的有效性.
主要方法:
- 罗尔集成深度运营商网络 (DeepONet) 来实现线下控制法律的获取.
- 双延迟深度决定性政策梯度 (TD3) 余量用于在线适应.
- 该框架在1D Kuramoto-Sivashinsky方程上进行了测试,这是空间时间混乱的基准.
主要成果:
- 与LQR相比,ROL减少了99.1%的系统能量,与纯TD3.3相比,LQR减少了64.3%,纯TD3.
- 在200个时代中,DeepONet实现了低训练损失 (7.8 × 10-6).
- 罗尔限制状态振幅是TD3的三倍,并更快地稳定了混乱.
结论:
- 将操作员学习与剩余策略优化相结合,提供了最先进的控制.
- ROL提供了一个样本效率高的方法来稳定混乱的部分微分方程.
- 这种方法对于流抑制和燃烧控制等应用具有可扩展性.
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