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方差问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-26 04:00

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。该算法旨在学习数据收集策略，使其对转移函数中的不确定性具有鲁棒性，这是当现实世界环境与模拟模型不同时的一个常见问题。与现有方法相比，所提出的方法对转移扰动的敏感性降低，并具有全局收敛的理论保证。 AI

影响这项研究可能导致更可靠、更高效的强化学习策略评估，减少昂贵的现实世界数据收集的需要。

排序理由该集群包含一篇在arXiv上发表的研究论文，详细介绍了一种用于强化学习的新算法。[lever_c_demoted from research: ic=1 ai=1.0]

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

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arXiv stat.ML TIER_1 English(EN) · Claire Chen, Shuze Daniel Liu, Licheng Luo, Rohan Chandra, Nan Jiang, Shangtong Zhang · 2026-08-26 04:00

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…