在合振荡器中服木乃伊,使用基于软演员-批判的强化学习
Jianpeng Ding1,2, Youming Lei1,2, Michael Small3
1School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China.
Chaos (Woodbury, N.Y.)
|January 15, 2025
概括
我们开发了一种无模型的深度强化学习方法,用于控制合振荡器中的奇默状态. 这种方法精确地定位了连贯和不连贯的领域,为复杂的动态系统提供了多功能控制.
科学领域:
- 复杂的系统复杂的系统.
- 非线性动力学是一种非线性动力学.
- 统计物理 统计物理
背景情况:
- 奇美拉状态,在合振荡器系统中共存的连贯和不连贯域的独特模式,在非线性动力学中提出了一个迷人的挑战.
- 现有的控制方法通常需要详细的系统知识,这限制了它们的适用性.
- 了解和控制嵌合体状态对于从神经科学到电网的应用至关重要.
研究的目的:
- 提出一种普遍的,无模型的方法来控制合振荡器中的奇默状态.
- 精确操纵嵌合体状态内的连贯和不连贯域的空间位置.
- 证明拟议的控制策略的稳定性和多功能性.
主要方法:
- 利用深度强化学习,特别是软演员-批判算法,来学习控制政策.
- 基于局部顺序参数设计奖励函数,以指导对奇默状态的控制.
- 在局部合的Kuramoto振荡器和非局部合的FitzHugh-Nagumo模型上测试该方法.
主要成果:
- 提出的方法成功地控制了虚构状态,将域限制在所需位置,独立于初始条件和合方案.
- 通过调整控制参数,演示了产生各种嵌合体模式的能力,包括单头,多头和交替嵌合体.
- 展示了该方法在不同网络大小的通用性,以及其能够稳定木乃伊状态,防止漂移和崩.
结论:
- 深度强化学习提供了一个强大而通用的框架,用于控制复杂的动态状态,如模拟生物.
- 该方法的无模型性质提高了它对各种振荡系统的实际适用性.
- 这种方法可以精确地控制嵌合体状态的空间,为研究和潜在应用开辟了新的途径.
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