Learning Hierarchical Skill Policies with Offline Quality-Diversity Reinforcement Learning

• URL: http://arxiv.org/abs/2608.19684v1
• Date: Thu, 20 Aug 2026 06:20:03 GMT
• Title: Learning Hierarchical Skill Policies with Offline Quality-Diversity Reinforcement Learning
• Abstract summary: We introduce QDOS (Quality-Diversity Offline Skill learning), a unified pipeline for robust offline-to-online learning.<n>By providing robust and task-relevant skill representations, QDOS significantly improves the quality of the embedded skill space used by the low-level policy.
• Score: 44.603295025494674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies investigate how to leverage pre-collected datasets to improve the policy performance and sample efficiency of RL. One promising approach to achieve this goal is to employ a two-stage strategy: In the first stage, diverse skills are extracted as a low-level policy from a given dataset, and a high-level policy is trained to solve a specific task in the second stage. Typically, extraction of the low-level policy is performed based on unsupervised learning such as trajectory VAE. However, a limitation of this approach is that the quality of the low-level policy highly depends on the quality of the dataset. To address this issue, we introduce QDOS (Quality-Diversity Offline Skill learning), a unified pipeline for robust offline-to-online learning. Our approach incorporates an Advantage-Weighted Quality-Diversity pretraining objective, which weights the skill extraction and diversity objectives by the estimated advantage of each trajectory segment. This approach allows the model to extract diverse and high-value skills. By providing robust and task-relevant skill representations, QDOS significantly improves the quality of the embedded skill space used by the low-level policy. We further integrate this with a dual dataset reuse strategy, where offline data is used both for skill pretraining and for populating the online replay buffer via pseudo-labeling. Experiments demonstrate that QDOS significantly outperforms strong baselines in structured manipulation tasks and unstructured locomotion tasks, confirming its ability to accelerate exploration and improve final returns in challenging sparse-reward domains.

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