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English(EN) Decision-Focused Learning in MDPs: An Occupancy Measure Approach

新的MDP决策导向学习方法可降低遗憾值和计算成本

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的马尔可夫决策过程（MDP）决策导向学习（DFL）方法，解决了现有方法的扩展性限制。通过将MDP重新表述为基于占用测度的线性规划（LP），该新技术推导出了闭式梯度，并使用增强拉格朗日代理和随机行草图来处理不连续梯度和大状态空间。与之前的基于KKT的DFL和两阶段基线相比，该方法在各种任务中实现了更低的遗憾值和显著降低的计算成本。 AI

影响 通过改进顺序决策问题的学习算法，这项研究有可能使复杂AI系统中的决策更加可扩展和高效。

排序理由 该项目是一篇学术论文，详细介绍了一种用于马尔可夫决策过程的决策导向学习新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的MDP决策导向学习方法可降低遗憾值和计算成本

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该项目是一篇学术论文，详细介绍了一种用于马尔可夫决策过程的决策导向学习新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zihao Zhao, Ashwath K. Karunakaram, Ali Eshragh, Yuexing Li, Kai Wang ·

    MDP中的决策导向学习：一种占用测度方法

    arXiv:2610.08384v1 Announce Type: new Abstract: In this work, we consider decision-focused learning (DFL) for a Markov decision process (MDP), where existing methods differentiate through the KKT conditions of the Bellman equation and require solving a linear system over all stat…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    MDP中的决策导向学习：一种占用测度方法

    In this work, we consider decision-focused learning (DFL) for a Markov decision process (MDP), where existing methods differentiate through the KKT conditions of the Bellman equation and require solving a linear system over all state-action pairs, limiting its scalability. We add…

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