Offline reinforcement learning (RL) learns policies from a fixed dataset, but often requires large amounts of data. The challenge arises when labeled datasets are expensive, especially when rewards have to be provided by human labelers for large datasets. In contrast, unlabelled data tends to be less expensive. This situation highlights the importance of finding effective ways to use unlabelled data in offline RL, especially when labelled data is limited or expensive to obtain. In this paper, we present the algorithm to utilize the unlabeled data in the offline RL method with kernel function approximation and give the theoretical guarantee. We present various eigenvalue decay conditions of $\mathcal{H}_k$ which determine the complexity of the algorithm. In summary, our work provides a promising approach for exploiting the advantages offered by unlabeled data in offline RL, whilst maintaining theoretical assurances.

本研究解决了离线强化学习中标签数据成本高昂的问题，提出了一种利用无标签数据的方法。论文中介绍的算法基于核函数逼近，并提供了理论保证，展现了在标签数据有限时有效利用无标签数据的潜力，可能显著提升离线强化学习的效率。

通过核函数逼近利用无标签数据共享在离线强化学习中的应用