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

2024-08-22

arXiv.org

AI Summary

Offline reinforcement learning (RL) suffers from the scarcity and high cost of labeled data—particularly human-provided reward annotations—while abundant unlabeled trajectory data remains underutilized. To address this, we propose a novel offline RL framework that effectively incorporates unlabeled trajectories by introducing kernel function approximation into the offline RL paradigm. Specifically, we model both policies and value functions in a reproducing kernel Hilbert space (RKHS), and establish theoretical guarantees grounded in the eigenvalue decay of the RKHS kernel operator. Our method significantly improves policy performance under stringent labeling budgets and provides a provable upper bound on sample complexity. To the best of our knowledge, this is the first approach that simultaneously achieves rigorous theoretical foundations—via nonparametric statistical analysis in RKHS—and practical efficacy in leveraging unlabeled data for offline RL.

Technology Category

• Machine Learning: Online Learning & Bandits
• Reasoning under Uncertainty: Stochastic Optimization
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Utilizing unlabeled data in offline reinforcement learning
• Reducing dependency on expensive labeled datasets
• Ensuring theoretical guarantees with kernel approximation

Innovation

• Uses kernel function approximation for unlabeled data
• Provides theoretical guarantees for offline RL
• Analyzes eigenvalue decay conditions for complexity