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English(EN) Q-Learning with Stable Infinite-Dimensional Linear Function Approximation

新的Q学习框架提供稳定的无限维线性逼近

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种使用无限维线性函数逼近的稳定Q学习新框架。该方法通过重建和压缩算子来保持贝尔曼收缩，从而解决了传统Q学习中的不稳定性问题。该框架的学习变量是紧致潜在度量空间上的系数场，并提供了\(\\widetilde O(n^{-1/2})\)阶的收敛界限。该方法为理解统计难度提供了强大的抽象，并能适应潜在空间的几何形状和光滑度。 AI

影响 在强化学习算法方面引入了理论进展，有可能提高未来AI应用的稳定性和效率。

排序理由 学术论文，详细介绍了Q学习的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的Q学习框架提供稳定的无限维线性逼近

本文如何被排名

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学术论文，详细介绍了Q学习的新理论框架。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Shengbo Wang ·

    基于稳定无限维线性函数逼近的Q学习

    arXiv:2608.22636v1 Announce Type: cross Abstract: Q-learning with linear function approximation can be unstable because an arbitrary approximation architecture need not preserve the Bellman contraction. We develop a stable infinite-dimensional linear function approximation framew…

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