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新策略方法将黑盒模拟器调用次数减少了 10 倍

研究人员开发了一种优化黑盒模拟器的新方法,这些模拟器通常不可微分且计算成本高昂。该方法涉及学习一个策略来指导可微分代理模型的训练。然后,利用该代理模型的梯度通过 SGD 优化模拟参数。该技术分摊了下游优化的成本,与现有的贝叶斯优化和数值优化等方法相比,所需的模拟器调用次数减少了 10 倍。 AI

影响 该方法可以显著降低训练和优化复杂模拟模型的计算成本,从而加速各个科学和工程领域的研发。

排序理由 该集群包含一篇详细介绍黑盒优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新策略方法将黑盒模拟器调用次数减少了 10 倍

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该集群包含一篇详细介绍黑盒优化新方法的学术论文。[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) · Tim Bakker, Fabio Valerio Massoli, Thomas Hehn, Tribhuvanesh Orekondy, Arash Behboodi ·

    模拟,快与慢:为黑盒优化学习策略

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