QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
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 standard RL assumptions. History-aware sequence models such as Decision Transformer (DT) are a natural fit for long-term dependency modeling, yet pure attention is inefficient and brittle when handling local Markovian structure and long-range context simultaneously. Although recent hybrid architectures (e.g., LSDT) introduce local extractors to improve local dependencies modeling, the fixed-window extraction cannot adapt its effective memory to varying dependency len...
| Authors | Xing Lei, Jincheng Wang, Xuetao Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 May 2026 |
| Last updated at source | 8 May 2026 |
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