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English(EN) Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch

分析强化学习策略梯度方法在分布失配下的表现

作者 PulseAugur 编辑部 · [1 个来源] ·

本文分析了强化学习中的在线策略梯度方法，重点关注分布失配的影响。研究在有偏更新下，为表格型直接和softmax参数化方法的目标值建立了全局收敛性。对于表格型softmax策略，推导出了渐近目标误差的精确系数，提供了一个评估分布失配是加速还是减缓收敛的标准。该分析扩展到更一般的策略参数化，推导出了状态分布失配和诱导的归一化策略梯度失配的界限，这些界限随着贴现因子接近一而减小。 AI

影响 为策略梯度方法的鲁棒性提供了理论见解，可能为未来的算法设计提供信息。

排序理由 该集群包含一篇详细阐述强化学习方法理论分析的学术论文。[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 →

分析强化学习策略梯度方法在分布失配下的表现

本文如何被排名

Signal score
4 / 100
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Newsworthiness bucket
Tool
该集群包含一篇详细阐述强化学习方法理论分析的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, other
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High
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Same-day
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Weizhen Wang, Xinzhe Zhou, Jianping He, Xiaoming Duan ·

    Analysis of On-policy Policy Gradient Methods under the Distribution Mismatch

    arXiv:2503.22244v3 Announce Type: replace Abstract: Policy gradient methods are among the most successful approaches for solving challenging reinforcement learning problems. Despite their empirical success, many state-of-the-art policy gradient algorithms for discounted problems …

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