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English(EN) Deep Learning for Dynamic Programming with Recursive Utility

新的深度学习算法解决了高维动态规划问题

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为确定性等价学习（CEL）的新型深度学习算法，用于解决具有递归效用的复杂高维动态规划问题。这种无网格、基于仿真的方法使用神经网络直接学习确定性等价值，无需状态转换的显式表示或可微性。CEL算法在各种金融应用中，包括鲁棒控制和资产配置，都对值函数和策略函数进行了准确的近似，贝尔曼误差在 1.0e-4 到 1.0e-3 的范围内。 AI

影响 该算法可以实现高维场景下更复杂的金融建模和风险管理。

排序理由 介绍新算法及其应用的学术论文。

在 arXiv stat.ML 阅读 →

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

新的深度学习算法解决了高维动态规划问题

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Xianhua Peng, Wu Guo ·

    面向递归效用的深度学习用于动态规划

    arXiv:2607.04278v1 Announce Type: cross Abstract: We propose the first deep learning algorithm, the Certainty Equivalent Learning (CEL) algorithm, for solving high-dimensional discrete-time dynamic programming problems with recursive utility. Dynamic programming with recursive ut…

  2. arXiv stat.ML TIER_1 English(EN) · Wu Guo ·

    用于递归效用的深度学习

    We propose the first deep learning algorithm, the Certainty Equivalent Learning (CEL) algorithm, for solving high-dimensional discrete-time dynamic programming problems with recursive utility. Dynamic programming with recursive utility is numerically challenging because the recur…

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