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QHyer model enhances offline goal-conditioned RL with adaptive history compression

By PulseAugur Editorial · [1 sources] ·

Researchers have developed QHyer, a novel approach for offline goal-conditioned reinforcement learning that addresses challenges posed by partially observable and history-dependent datasets. QHyer utilizes a Q-estimator to guide policy stitching and a hybrid Attention-Mamba backbone for adaptive history compression. Experiments show QHyer achieves state-of-the-art performance on both non-Markovian and Markovian datasets. AI

IMPACT Introduces a new method for goal-conditioned reinforcement learning that improves performance on complex datasets.

RANK_REASON This is a research paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

QHyer model enhances offline goal-conditioned RL with adaptive history compression

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This is a research paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xing Lei, Jincheng Wang, Xuetao Zhang, Donglin Wang ·

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

    arXiv:2605.01862v1 Announce Type: new 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 standa…

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