English(EN) Adaptive Resolving Methods for Markov Decision Processes with Function Approximations

新算法为马尔可夫决策过程提供高效解决方案

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

研究人员开发了一种新颖的算法，用于高效解决利用函数逼近的马尔可夫决策过程（MDP）问题。该新方法基于线性规划重构，并随着更多转移样本的可用而迭代地求解一个简化的线性系统。该算法实现了实例相关的目标差距和约束残差，理论保证为 $O(1/\sqrt{N})$，且与某些问题参数无关。 AI

影响这项研究可能导致复杂系统中更高效的决策制定，并可能影响机器人和强化学习等领域。

排序理由这是一篇详细介绍解决马尔可夫决策过程新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Jiashuo Jiang, Yinyu Ye, Yiming Zong · 2026-09-04 04:00

带函数逼近的马尔可夫决策过程的自适应求解方法

arXiv:2505.12037v2 Announce Type: replace Abstract: Learning the optimal policy for Markov decision process problems (MDPs) from samples is a fundamental problem in online and data-driven decision-making. Function approximations are usually deployed to handle large or infinite st…