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English(EN) PAWS: Preference Learning with Advantage-Weighted Segments

PAWS方法通过基于片段的优势函数改进强化学习

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的首选项学习强化学习方法PAWS，该方法解决了关键的训练-推理不匹配问题。通过利用片段级优势函数进行策略更新，PAWS将效用训练与优化对齐，保留了首选项信息，并避免了不可靠的每步信号。在机器人操作和运动任务上的实验表明，PAWS的性能优于现有方法，突显了分布一致的首选项学习的重要性。 AI

影响 通过分布一致的首选项学习，改进了时间信用分配和策略优化，从而增强了强化学习。

排序理由 该集群包含一篇详细介绍一种新的基于首选项的强化学习方法的学术论文。

在 arXiv cs.LG 阅读 →

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PAWS方法通过基于片段的优势函数改进强化学习

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Aleksandar Taranovic, Onur Celik, Niklas Freymuth, Ge Li, Serge Thilges, Huy Le, Tai Hoang, Rania Rayyes, Gerhard Neumann ·

    PAWS：具有优势加权片段的首选项学习

    arXiv:2606.11982v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) learns policies from human trajectory-level comparisons, avoiding explicit reward design and expert demonstrations. Existing methods typically train utility functions on trajectory or s…

  2. arXiv cs.LG TIER_1 English(EN) · Gerhard Neumann ·

    PAWS: 具有优势加权片段的首选项学习

    Preference-based reinforcement learning (PbRL) learns policies from human trajectory-level comparisons, avoiding explicit reward design and expert demonstrations. Existing methods typically train utility functions on trajectory or segment-level preferences while relying on per-st…

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