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Lyapunov Exponent as Physics-Informed Reward for RL Stabilization

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员提出使用 Lyapunov 特征指数 (LCE) 作为强化学习 (RL) 任务的密集奖励信号，特别是用于稳定倒立摆。这种新颖的方法不仅使 RL 智能体能够实现已知的 Kapitza 摆的振荡运动，还能抑制摆的枢轴运动，从而实现稳定、直立的位置。该研究强调了物理信息奖励在提升 RL 能力方面的潜力。 AI

影响 通过在强化学习中利用物理信息奖励，这项研究可能带来更高效、更稳定的机器人技术和其他应用中的控制系统。

排序理由 该集群包含一篇详细介绍新颖研究方法的学术论文。

在 arXiv cs.LG 阅读 →

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Lyapunov Exponent as Physics-Informed Reward for RL Stabilization

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报道来源 [2]

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

    Lyapunov指数作为物理信息密集奖励：RL发现超越Kapitza摆的稳定化

    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指数作为物理信息密集奖励：RL发现超越Kapitza摆的稳定化

    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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