Learning Hierarchical Skill Policies with Offline Quality-Diversity Reinforcement Learning

2026-08-20

AI Summary

本文提出QDOS方法，通过结合优势加权的质量多样性预训练目标和双数据集重用策略，从离线数据中提取多样化高质量技能以改进强化学习任务性能。

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.

Problem

• offline reinforcement learning
• quality-diversity
• skill extraction
• sample efficiency

Innovation

• Quality-Diversity
• Advantage-Weighted
• Offline-to-Online Learning
• Skill Pretraining
• Dual Dataset Reuse