Unsupervised Data Generation for Offline Reinforcement Learning: A Perspective from Model

• URL: http://arxiv.org/abs/2506.19643v1
• Date: Tue, 24 Jun 2025 14:08:36 GMT
• Title: Unsupervised Data Generation for Offline Reinforcement Learning: A Perspective from Model
• Authors: Shuncheng He, Hongchang Zhang, Jianzhun Shao, Yuhang Jiang, Xiangyang Ji,
• Abstract summary: offline reinforcement learning (RL) recently gains growing interests from RL researchers.<n>The performance of offline RL suffers from the out-of-distribution problem, which can be corrected by feedback in online RL.<n>In this paper, we first build a bridge over the batch data and the performance of offline RL algorithms theoretically.<n>We show that in task-agnostic settings, a series of policies trained by unsupervised RL can minimize the worst-case regret in the performance gap.
• Score: 57.20064815347607
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Offline reinforcement learning (RL) recently gains growing interests from RL researchers. However, the performance of offline RL suffers from the out-of-distribution problem, which can be corrected by feedback in online RL. Previous offline RL research focuses on restricting the offline algorithm in in-distribution even in-sample action sampling. In contrast, fewer work pays attention to the influence of the batch data. In this paper, we first build a bridge over the batch data and the performance of offline RL algorithms theoretically, from the perspective of model-based offline RL optimization. We draw a conclusion that, with mild assumptions, the distance between the state-action pair distribution generated by the behavioural policy and the distribution generated by the optimal policy, accounts for the performance gap between the policy learned by model-based offline RL and the optimal policy. Secondly, we reveal that in task-agnostic settings, a series of policies trained by unsupervised RL can minimize the worst-case regret in the performance gap. Inspired by the theoretical conclusions, UDG (Unsupervised Data Generation) is devised to generate data and select proper data for offline training under tasks-agnostic settings. Empirical results demonstrate that UDG can outperform supervised data generation on solving unknown tasks.

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