Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement Learning

TL;DR

This paper introduces a kernel function approximation method for offline reinforcement learning that effectively leverages unlabeled data, providing theoretical guarantees and addressing data scarcity issues.

Contribution

The paper proposes a novel algorithm that utilizes unlabeled data in offline RL with kernel approximation, supported by theoretical analysis of eigenvalue decay conditions.

Findings

Algorithm effectively uses unlabeled data in offline RL

Provides theoretical guarantees for the approach

Analyzes eigenvalue decay conditions affecting complexity

Abstract

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.