
























Abstract:Offline goal-conditioned reinforcement learning (GCRL) is a practical reinforcement learning paradigm that aims to learn goal-conditioned policies from reward-free offline data. Despite recent advances in hierarchical architectures such as HIQL, long-horizon control in offline GCRL remains challenging due to the limited expressiveness of Gaussian policies and the inability of high-level policies to generate effective subgoals. To address these limitations, we propose the goal-conditioned mean flow policy, which introduces an average velocity field into hierarchical policy modeling for offline GCRL. Specifically, the mean flow policy captures complex target distributions for both high-level and low-level policies through a learned average velocity field, enabling efficient action generation via one-step sampling. Furthermore, considering the insufficiency of goal representation, we introduce a LeJEPA loss that repels goal representation embeddings during training, thereby encouraging more discriminative representations and improving generalization. Experimental results show that our method achieves strong performance across both state-based and pixel-based tasks in the OGBench benchmark.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.08960 [cs.LG] |
| (or arXiv:2604.08960v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.08960 arXiv-issued DOI via DataCite (pending registration) |
From: Zhiqiang Dong [view email]
[v1]
Fri, 10 Apr 2026 05:04:29 UTC (139 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。