QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL

• URL: http://arxiv.org/abs/2605.01862v2
• Date: Fri, 08 May 2026 03:23:44 GMT
• Title: QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
• Abstract summary: offline goal-conditioned RL (GCRL) learns goal-reaching policies from static datasets.<n>Real-world datasets are often partially observable and history-dependent, exhibiting a mix of Markovian and non-Markovian.<n>textbfQHyer introduces a gated Hybrid Attention-Mamba backbone that performs content-adaptive history compression.
• Score: 34.9638718909932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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 lengths in temporally heterogeneous settings, often truncating long-range context rather than compressing its content adaptively. Moreover, sequential offline GCRL faces a key bottleneck: under sparse rewards, return-to-go (RTG) becomes non-discriminative across sub-trajectories, providing little guidance signal for stitching goal-reaching behaviors from diverse demonstrations. To address these, we propose \textbf{QHyer}, which replaces RTG with a flow-parameterized, state-conditioned goal-reaching Q-estimator to support stitching across demonstrations, and introduces a gated Hybrid Attention-Mamba backbone that performs content-adaptive history compression while preserving local dynamics. Extensive experiments demonstrate that \textbf{QHyer} achieves state-of-the-art performance on both non-Markovian and Markovian datasets, validating its effectiveness for diverse scenarios.

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