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arXiv · 2503.20176

Offline Reinforcement Learning with Discrete Diffusion Skills

RuiXi Qiao·Jie Cheng·Xingyuan Dai·Yonglin Tian·Yisheng Lv
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Abstract

Skills have been introduced to offline reinforcement learning (RL) as temporal abstractions to tackle complex, long-horizon tasks, promoting consistent behavior and enabling meaningful exploration. While skills in offline RL are predominantly modeled within a continuous latent space, the potential of discrete skill spaces remains largely underexplored. In this paper, we propose a compact discrete skill space for offline RL tasks supported by state-of-the-art transformer-based encoder and diffusion-based decoder. Coupled with a high-level policy trained via offline RL techniques, our method establishes a hierarchical RL framework where the trained diffusion decoder plays a pivotal role. Empirical evaluations show that the proposed algorithm, Discrete Diffusion Skill (DDS), is a powerful offline RL method. DDS performs competitively on Locomotion and Kitchen tasks and excels on long-horizon tasks, achieving at least a 12 percent improvement on AntMaze-v2 benchmarks compared to existing offline RL approaches. Furthermore, DDS offers improved interpretability, training stability, and online exploration compared to previous skill-based methods.

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RuiXi Qiao, Jie Cheng, Xingyuan Dai, Yonglin Tian, Yisheng Lv. 2025-03-26. Offline Reinforcement Learning with Discrete Diffusion Skills. https://arxiv.org/abs/2503.20176

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