Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement Learning
This work addresses the challenge of data scarcity in offline RL for applications where labeled data is costly, offering a method to utilize cheaper unlabeled data, though it appears incremental in nature.
The paper tackles the problem of expensive labeled data in offline reinforcement learning by proposing an algorithm that leverages unlabeled data through kernel function approximation, with theoretical guarantees on complexity based on eigenvalue decay conditions.
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.