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Lyapunov Exponent as Physics-Informed Reward for RL Stabilization

By PulseAugur Editorial · [2 sources] ·

Researchers have proposed using the Lyapunov characteristic exponent (LCE) as a dense reward signal for reinforcement learning (RL) tasks, specifically for stabilizing an inverted pendulum. This novel approach enabled an RL agent not only to achieve the known Kapitza pendulum's oscillatory motion but also to damp the pendulum's pivoting, resulting in a stable, upright position. The study highlights the potential of physics-informed rewards in advancing RL capabilities. AI

IMPACT This research could lead to more efficient and stable control systems in robotics and other applications by leveraging physics-informed rewards in reinforcement learning.

RANK_REASON The cluster contains an academic paper detailing a novel research methodology.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Lyapunov Exponent as Physics-Informed Reward for RL Stabilization

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Slava Andrejev ·

    Lyapunov Exponent as Physics-Informed Dense Reward: RL Discovery of Stabilization Beyond the Kapitza Pendulum

    arXiv:2607.14001v1 Announce Type: new Abstract: 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 t…

  2. arXiv cs.LG TIER_1 English(EN) · Slava Andrejev ·

    Lyapunov Exponent as Physics-Informed Dense Reward: RL Discovery of Stabilization Beyond the Kapitza Pendulum

    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 pendu…

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