  In this paper, we investigate an offline reinforcement learning (RL) problem
where datasets are collected from two domains. In this scenario, having
datasets with domain labels facilitates efficient policy training. However, in
practice, the task of assigning domain labels can be resource-intensive or
infeasible at a large scale, leading to a prevalence of domain-unlabeled data.
To formalize this challenge, we introduce a novel offline RL problem setting
named Positive-Unlabeled Offline RL (PUORL), which incorporates
domain-unlabeled data. To address PUORL, we develop an offline RL algorithm
utilizing positive-unlabeled learning to predict the domain labels of
domain-unlabeled data, enabling the integration of this data into policy
training. Our experiments show the effectiveness of our method in accurately
identifying domains and learning policies that outperform baselines in the
PUORL setting, highlighting its capability to leverage domain-unlabeled data
effectively.
