English(EN) Sample Complexity of Linear Quadratic Regulator Without Initial Stability

新的LQR算法绕过稳定性要求，提高样本复杂度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员为线性二次调节器（LQR）问题开发了一种新的递推视界算法，该算法解决了系统动力学未知的问题。这种新颖的方法通过不需要两点梯度估计并保持相同的样本复杂度来改进现有方法。一项关键的进展是消除了对初始稳定策略的需求，从而使该算法更具广泛适用性。该研究还利用黎曼距离精炼了通过Riccati算子的误差传播分析，从而提高了样本复杂度并保证了收敛性。 AI

影响为控制系统引入了更具广泛适用性和效率的算法，可能影响使用强化学习进行动态控制的领域。

排序理由详细介绍控制论问题新算法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard · 2026-09-03 04:00

无初始稳定性下的线性二次调节器的样本复杂度

arXiv:2502.14210v4 Announce Type: replace-cross Abstract: Inspired by REINFORCE, we introduce a novel receding-horizon algorithm for the Linear Quadratic Regulator (LQR) problem with unknown dynamics. Unlike prior methods, our algorithm avoids reliance on two-point gradient estim…

本文如何被排名

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无初始稳定性下的线性二次调节器的样本复杂度

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