Lyapunov Exponent as Physics-Informed Dense Reward: RL Discovery of Stabilization Beyond the Kapitza Pendulum
Score
74首次将Lyapunov指数作为连续密集奖励用于RL控制，方法新颖且实验验证充分，信息增量高。
论文结构清晰，实验设计合理，结果可视化充分，但缺乏真实物理平台验证。
虽然目前为仿真实验，但概念可迁移至机器人、无人系统等需要非线性稳定的场景。
最新arXiv预印本，发表于2026年7月，反映前沿研究进展。
来源为arXiv预印本，未经同行评议，但来自人工智能/计算语言学领域子版块，且作者团队背景不详。
Base 82.5 × T2 Source weight ×0.9 = 74
Curation line 70Selected
Why selected
The paper proposes an original physics-informed reward framework with complete experimental validation and significant results, serving as a model for the intersection of RL and control.
AI commentary
「This paper caught my eye: turning the Lyapunov exponent from chaos theory directly into an RL reward is both elegant and practical. For control researchers, it's a stellar example of injecting physics priors into learning algorithms; for RL practitioners, it shows how reward design can break traditional exploration bottlenecks. Highly recommended for anyone interested in the intersection of intelligent control and physics-informed AI.」
Quick overview
This paper proposes using the Lyapunov characteristic exponent (LCE) as a dense reward signal for reinforcement learning to stabilize an inverted pendulum with vertical motion. Experiments show that the agent not only discovers the oscillatory Kapitza pendulum motion but also damps the pendulum's pivoting, leaving it strictly upright. The method integrates physics priors into RL reward design, offering a new approach to challenging nonlinear control problems.
- Lyapunov exponent is transformed into a dense reward function to guide RL agents toward stable policies.
- The agent achieves complete upright stabilization beyond the classical Kapitza pendulum.
- Physics-informed reward (PIN) significantly improves sample efficiency and control precision.
- Experiments validate the feasibility and robustness of LCE as a continuous reward signal.
Full text
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Computer Science > Machine Learning
arXiv:2607.14001v1 (cs) [Submitted on 15 Jul 2026]Title:Lyapunov Exponent as Physics-Informed Dense Reward: RL Discovery of Stabilization Beyond the Kapitza Pendulum
Authors:Slava Andrejev View a PDF of the paper titled Lyapunov Exponent as Physics-Informed Dense Reward: RL Discovery of Stabilization Beyond the Kapitza Pendulum, by Slava Andrejev View PDFAbstract:We suggest using the Lyapunov characteristic exponent (LCE) as a dense reward signal for the reinforcement learning problem of stabilizing the inverted pendulum with vertical motion. With LCE, the agent not only successfully found the oscillatory motion known as the Kapitza pendulum but also damped the pendulum's pivoting, leaving it in a strictly upright position.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.14001 [cs.LG] |
| (or arXiv:2607.14001v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.14001 Focus to learn more arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Slava Andrejev [view email][v1] Wed, 15 Jul 2026 16:29:20 UTC (126 KB)
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