高斯-牛顿时间差异学习与非线性函数近似
IEEE transactions on neural networks and learning systems
|February 25, 2026
概括
一种新的高斯-牛顿时差 (GNTD) 学习方法通过非线性近似改进了Q学习. GNTD提供了更好的样本复杂性和强化学习基准的更快的融合.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 强化学习是一种强化学习.
背景情况:
- Q-learning是一种基本的强化学习算法.
- 在Q学习中,非线性函数近似对于处理复杂状态空间至关重要.
- 现有的时间差 (TD) 方法面临的挑战是样本复杂性和收率.
研究的目的:
- 提出一种新的高斯-牛顿时间差 (GNTD) 学习方法.
- 解决现有方法在样本复杂性和趋同性方面的局限性.
- 通过非线性函数近似实现高效稳定的Q学习.
主要方法:
- 拟议的GNTD方法使用高斯-牛顿 (GN) 步骤来优化平均平方贝尔曼误差 (MSBE) 的变体.
- 目标网络被用来缓解与双重抽样相关的问题.
- 不准确的GN步骤被分析为使用矩阵代的高效计算.
- 非对称的有限样本收保证是在温和条件下得出的.
主要成果:
- 对于具有 ReLU 激活的神经网络,GNTD 实现了 $\tilde {\mathcal {O}}(\varepsilon ^{-1}) $ 的改进样本复杂性,优于现有的 TD 方法.
- 对于一般的平滑函数近似,GNTD 建立了一个 $\tilde {\mathcal {O}}(\varepsilon ^{-1.5}) $ 的样本复杂度.
- 关于强化学习基准的广泛实验表明,与TD类型方法相比,GNTD产生更高的回报和更快的融合.
结论:
- 该GNTD学习方法提供了一个理论上健全和经验上有效的方法,用于Q学习与非线性函数近似.
- GNTD显著提高了样本效率和融合速度,特别是在深度强化学习环境中.
- 提出的方法为解决复杂的强化学习问题提供了有希望的进展.
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