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English(EN) Bridging the Gap between Newton-Raphson Method and Regularized Policy Iteration

新理论连接牛顿-拉夫逊方法与正则化策略迭代

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员在正则化马尔可夫决策过程（RMDPs）的应用中，正式建立了牛顿-拉夫逊方法与正则化策略迭代（RPI）之间的等价性。这种联系，尤其是在正则化项为香农熵时，使得对RPI进行统一的理论分析并开发加速算法成为可能。研究表明，RPI表现出局部二次收敛性，并提出了一种实现三阶局部收敛的新算法，该算法得到了数值实验的支持。 AI

影响 推进了对强化学习中正则化理论的理解，可能导致更高效的算法设计。

排序理由 该集群包含一篇详细介绍强化学习算法理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新理论连接牛顿-拉夫逊方法与正则化策略迭代

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该集群包含一篇详细介绍强化学习算法理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zeyang Li, Chuxiong Hu, Yunan Wang, Guojian Zhan, Jie Li, Yao Lyu, Shengbo Eben Li ·

    连接牛顿-拉夫逊方法与正则化策略迭代的桥梁

    arXiv:2310.07211v2 Announce Type: replace Abstract: Regularization is a cornerstone of modern reinforcement learning. Regularized policy iteration (RPI) provides a fundamental scheme for solving regularized Markov decision processes (RMDPs), and the widely used soft actor-critic …

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