arXiv cs.AI· Junhyeon Bae, Jaebak Hwang, Gwanwoo Choi, Minung Kim, Seungyul Han·· 14 小时前AI 评分31
BGP：用分支感知图规划逃离离线目标条件强化学习中的路径海市蜃楼
Escaping Path Mirages in Offline Goal-Conditioned Reinforcement Learning
AI 导读
针对离线目标条件强化学习中的“路径海市蜃楼”问题，研究者提出分支感知图规划（BGP），将高变异性点识别为图节点，并用分段学习设计目标条件边，以避开不可靠分支、减少路径上的随机性。在多种 OGBench 任务上，BGP 大幅超越此前最先进的离线目标条件 RL 方法，并已被 NeurIPS 2026 接收。
正文 · 原文
Abstract:Offline goal-conditioned reinforcement learning remains challenging in stochastic long-horizon settings, where compounding value estimation errors hinder reliable goal reaching. While prior methods have sought to address this challenge through various approaches, a key challenge remains path mirage, where agents overcommit to spuriously successful trajectories in offline datasets induced by stochasticity, often leading to failure on difficult tasks. To address this issue, we propose Branch-aware Graph Planning (BGP), which captures stochastic branching structures by identifying high-variability points as graph nodes and designing goal-conditioned edges with segmented learning to avoid unreliable branches and reduce unnecessary stochasticity along paths. As a result, BGP enables reliable goal reaching even in highly stochastic long-horizon environments, and experiments on diverse OGBench tasks show that it substantially outperforms prior state-of-the-art offline goal-conditioned RL methods. Our implementation is available at this https URL.
| Comments: | 10 pages (main), 34 pages in total. Accepted to NeurIPS 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.40130 [cs.LG] |
| (or arXiv:2610.40130v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40130 arXiv-issued DOI via DataCite |
Submission history
From: Seungyul Han [view email]
[v1]
Thu, 29 Oct 2026 14:21:50 UTC (9,903 KB)
来源：arXiv cs.AI · arxiv.org