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English(EN) Randomized Transport Maps for Model-Free Policy-Gradient Mean-Field Control

新的传输REINFORCE方法增强了均值场控制

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种用于离散时间均值场控制（MFC）的新型无模型策略梯度方法，称为Transport REINFORCE。该方法解决了MFC中策略同时影响系统动力学和整体种群分布的挑战。Transport REINFORCE通过扰动种群分布来估计其贡献，在标准REINFORCE估计器方面有所改进。该技术适用于有限和连续状态空间，具有一致性和误差界限的理论保证，并在数值实验中显示出积极的结果。 AI

影响 引入了一种新颖的均值场控制方法，有望提高强化学习智能体在复杂多智能体系统中的性能。

排序理由 该集群包含一篇详细介绍特定控制理论领域新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的传输REINFORCE方法增强了均值场控制

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该集群包含一篇详细介绍特定控制理论领域新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Adonis Jamal, Samy Mekkaoui, Yadh Hafsi, Huy\^en Pham ·

    用于无模型策略梯度均值场控制的随机输运图

    arXiv:2610.11619v1 Announce Type: cross Abstract: We develop a model-free policy gradient method for discrete-time mean-field control (MFC). In MFC, the policy affects the objective both through the controlled dynamics and through the population distribution. Standard REINFORCE e…

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