English(EN) Strong and Compact Policies for Submodular Markov Decision Processes via LP-Based Submodular Orienteering

新的基于线性规划的算法为子模态MDP提供了更强的策略

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种新的算法来解决子模态马尔可夫决策过程（MDP），这是一种具有广义奖励函数的序贯决策问题。该算法基于线性规划（LP）技术和Sherali-Adams层级思想，为子模态定向和子模态MDP提供了强大的近似保证。这项工作改进了先前的近似比，特别是对于子模态MDP，其先前的保证与时间范围成线性关系。 AI

影响为与强化学习相关的序贯决策问题引入了改进的算法保证。

排序理由详细介绍特定类别决策问题的新算法和理论结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Lars Rohwedder, Rico Zenklusen · 2026-09-15 04:00

面向子模态马尔可夫决策过程的基于LP的子模态导向的强紧凑策略

arXiv:2609.15539v1 Announce Type: cross Abstract: Finding policies for Markov Decision Processes (MDPs) is a central problem in areas such as Reinforcement Learning and Operations Research. Here, we have to repeatedly choose an action that should be performed by an agent. Dependi…

本文如何被排名

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面向子模态马尔可夫决策过程的基于LP的子模态导向的强紧凑策略

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