在不稳定潜流形上通过策略优化实现系统稳定

IF 6.9 1区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
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引用次数: 0

摘要

稳定性是研究动态系统行为的基本要求。然而,通过强化学习来稳定动态系统具有挑战性,因为在不稳定性触发和数据变得毫无意义之前,只能在短时间内收集到少量数据。这项工作引入了一种强化学习方法,该方法是在不稳定动力学的潜在流形上制定的,因此可以从少量数据样本中训练出稳定策略。不稳定流形是最小的,因为它们包含最低维度的动态,而这些动态是学习保证稳定的策略所必需的。这与一般的潜在流形形成了鲜明对比,后者旨在近似所有稳定和不稳定系统动态,因此维度更高,通常需要更多数据。实验证明,即使是复杂的物理系统,所提出的方法也能从少量数据样本中实现稳定,而其他直接在系统状态空间或通用潜流形上运行的方法却无法做到这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
System stabilization with policy optimization on unstable latent manifolds
Stability is a basic requirement when studying the behavior of dynamical systems. However, stabilizing dynamical systems via reinforcement learning is challenging because only little data can be collected over short time horizons before instabilities are triggered and data become meaningless. This work introduces a reinforcement learning approach that is formulated over latent manifolds of unstable dynamics so that stabilizing policies can be trained from few data samples. The unstable manifolds are minimal in the sense that they contain the lowest dimensional dynamics that are necessary for learning policies that guarantee stabilization. This is in stark contrast to generic latent manifolds that aim to approximate all—stable and unstable—system dynamics and thus are higher dimensional and often require higher amounts of data. Experiments demonstrate that the proposed approach stabilizes even complex physical systems from few data samples for which other methods that operate either directly in the system state space or on generic latent manifolds fail.
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来源期刊
CiteScore
12.70
自引率
15.30%
发文量
719
审稿时长
44 days
期刊介绍: Computer Methods in Applied Mechanics and Engineering stands as a cornerstone in the realm of computational science and engineering. With a history spanning over five decades, the journal has been a key platform for disseminating papers on advanced mathematical modeling and numerical solutions. Interdisciplinary in nature, these contributions encompass mechanics, mathematics, computer science, and various scientific disciplines. The journal welcomes a broad range of computational methods addressing the simulation, analysis, and design of complex physical problems, making it a vital resource for researchers in the field.
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