English(EN) QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL

QHyer模型通过自适应历史压缩增强离线目标条件强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-05 04:00

研究人员开发了QHyer，一种用于离线目标条件强化学习的新方法，解决了部分可观察和历史依赖数据集带来的挑战。QHyer利用Q估计器指导策略拼接，并采用混合注意力-Mamba骨干进行自适应历史压缩。实验表明，QHyer在非马尔可夫和马尔可夫数据集上均取得了最先进的性能。 AI

影响为目标条件强化学习引入了一种新方法，提高了在复杂数据集上的性能。

排序理由这是一篇详细介绍强化学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Xing Lei, Jincheng Wang, Xuetao Zhang, Donglin Wang · 2026-05-05 04:00

QHyer：用于离线目标条件强化学习的Q条件混合注意力-Mamba Transformer

arXiv:2605.01862v1 Announce Type: new Abstract: Offline goal-conditioned RL (GCRL) learns goal-reaching policies from static datasets, but real-world datasets are often partially observable and history-dependent, exhibiting a mix of Markovian and non-Markovian that violate standa…