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English(EN) Robust Data-Collection Policy Learning for Low-Variance Online Policy Evaluation

新算法解决了强化学习策略评估中的高方差问题

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的双循环基于梯度的算法，以解决强化学习策略评估中的高方差问题。该方法专门处理转移函数中的不确定性，而这些不确定性通常会导致在模拟中训练的策略在现实世界环境中表现不佳。该算法推导出了新颖的转移方差梯度表达式，并建立了全局收敛保证，与现有方法相比，在应对转移扰动方面表现出更强的鲁棒性。 AI

影响 这项新算法可能导致在现实世界场景中进行更可靠、更有效的策略评估，从而减少昂贵的现实世界数据收集的需要。

排序理由 该集群描述了一篇详细介绍强化学习策略评估新算法的研究论文。

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 →

新算法解决了强化学习策略评估中的高方差问题

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该集群描述了一篇详细介绍强化学习策略评估新算法的研究论文。
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报道来源 [2]

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

    低方差在线策略评估的鲁棒数据收集策略学习

    In reinforcement learning policy evaluation, classic on-policy methods often suffer from high variance when estimating policy performance. To mitigate this issue, behavior policy search has been proposed to learn data-collecting policies tailored to reduce online evaluation varia…

  2. arXiv stat.ML TIER_1 English(EN) · Claire Chen, Shuze Daniel Liu, Licheng Luo, Rohan Chandra, Nan Jiang, Shangtong Zhang ·

    低方差在线策略评估的鲁棒数据收集策略学习

    arXiv:2608.24146v1 Announce Type: cross Abstract: In reinforcement learning policy evaluation, classic on-policy methods often suffer from high variance when estimating policy performance. To mitigate this issue, behavior policy search has been proposed to learn data-collecting p…

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