English(EN) Learning Goal-Reaching Quasimetric Geometry From Finite-Time Reachability

新的ReQRL方法增强了OGBench上以目标为条件的强化学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员开发了ReQRL，一种以目标为条件的强化学习（GCRL）的新方法，该方法将目标达成成本建模为准度量距离。该方法使用有限时间范围的可达性来约束Critic的值梯度，有效地将动态可达性与边界几何解耦。在OGBench数据集上，ReQRL与现有的准度量和离线GCRL方法相比，表现具有竞争力或更优。 AI

影响这项研究可以提高强化学习智能体在实现特定目标方面的效率和有效性。

排序理由该集群包含一篇详细介绍强化学习新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Daisuke Yamada, Travis Pence, Vikas Singh · 2026-10-03 04:00

从有限时间可达性中学习目标达成拟度量几何

arXiv:2610.00778v1 Announce Type: new Abstract: In goal-conditioned reinforcement learning (GCRL), quasimetric learning models goal-reaching costs as quasimetric distances, connecting local constraints to global value geometry. Its local constraints, however, should reflect the d…

本文如何被排名

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从有限时间可达性中学习目标达成拟度量几何

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