新方法将深度神经网络与线性规划相结合用于动态规划

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员提出了一种新颖的方法来近似解决动态规划问题，尤其是在强化学习中常见的高维场景下。该方法结合了深度神经网络和线性规划算法来最小化Bellman误差。通过在收益管理中的网络容量控制问题上进行演示，该方法显示出与现有基准相比具有竞争力。 AI

影响这项研究可能为人工智能和机器学习中的复杂优化问题带来更有效的解决方案。

排序理由该集群包含一篇关于arXiv的学术论文，详细介绍了一种新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Haining Yu · 2026-10-05 04:00

Bellman 误差最小化通过线性规划归一化

arXiv:2610.02730v1 Announce Type: new Abstract: This paper proposes a new functional approximation approach to reduce Bellman error in high-dimensional dynamic programming and Reinforcement Learning problems. Using a classic dynamic programming problem (network capacity control i…