Leveraging Unlabeled Data to Improve Offline Reinforcement Learning Across Two Domains

Core Concepts

A novel offline reinforcement learning problem setting, Positive-Unlabeled Offline RL (PUORL), is introduced to effectively utilize domain-unlabeled data in scenarios with two distinct domains. An algorithmic framework is proposed that leverages positive-unlabeled learning to predict domain labels and integrate the domain-unlabeled data into policy training.

Abstract

The content discusses an offline reinforcement learning (RL) problem where datasets are collected from two distinct domains. Typically, having domain labels facilitates efficient policy training. However, in practice, assigning domain labels can be resource-intensive or infeasible, leading to a prevalence of domain-unlabeled data.
To address this challenge, the authors introduce a novel offline RL problem setting called Positive-Unlabeled Offline RL (PUORL). In PUORL, only a subset of the data from one domain is labeled, while the rest is domain-unlabeled.
The authors propose an algorithmic framework for PUORL that leverages positive-unlabeled (PU) learning to train a domain identifier and utilize it in the offline RL phase. Experiments on D4RL datasets with a small ratio of domain-labeled data (3%) demonstrate that the proposed method can accurately identify the domain of data and train better agents than baselines, showcasing the ability to effectively leverage the large amount of domain-unlabeled data.

Stats

In the PUORL setting, we have two distinct MDPs, a positive MDP Mp and a negative MDP Mn, which share the same state and action spaces and reward function.
The dataset consists of positive data Dp, sampled from the positive MDP, and unlabeled data Du, sampled from both the positive and negative MDPs.

Quotes

"In practice, however, assigning domain labels is often costly and laborious, or sometimes infeasible."
"Given that the volume of data plays a pivotal role in the success of offline RL, only training on the labeled data can result in poorly performant policy. We thus need to devise a way to effectively utilize the plenty of domain-unlabeled data."

Key Insights Distilled From

Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two Domains

by Soichiro Nis... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07465.pdf

Deeper Inquiries

How can the proposed PUORL framework be extended to handle more than two domains

To extend the proposed PUORL framework to handle more than two domains, several modifications and enhancements can be implemented. One approach could involve adapting the domain identification component to a multi-class classification problem, where each domain is treated as a separate class. This would require redefining the problem formulation to accommodate multiple domain labels and adjusting the algorithmic framework to support multi-class classification tasks. Additionally, incorporating advanced machine learning techniques such as ensemble learning or deep learning models could improve the scalability and performance of the framework when dealing with a larger number of domains. By enhancing the domain identification process to handle multiple domains, the PUORL framework can be effectively extended to more complex scenarios with diverse domain structures.

What are the potential limitations or drawbacks of using positive-unlabeled learning for domain identification in the offline RL setting

While positive-unlabeled learning offers a promising approach for domain identification in the offline RL setting, there are potential limitations and drawbacks to consider. One key limitation is the reliance on accurate estimation of the mixture proportion between positive and unlabeled data, which can be challenging in practice and may introduce bias or errors in the domain identification process. Moreover, the performance of PU learning methods can be sensitive to the quality and representativeness of the positive data, leading to potential issues with generalization and robustness when dealing with noisy or imbalanced datasets. Additionally, the computational complexity of PU learning algorithms may increase with the size of the dataset, impacting scalability and efficiency in large-scale applications. Addressing these limitations through robust mixture proportion estimation techniques, data preprocessing methods, and algorithmic optimizations is crucial to mitigate the drawbacks of using positive-unlabeled learning for domain identification in offline RL.

How might the insights from this work on leveraging domain-unlabeled data be applied to other areas of machine learning beyond offline RL

The insights gained from leveraging domain-unlabeled data in offline RL can be applied to various other areas of machine learning beyond RL. For instance, in semi-supervised learning tasks, where labeled data is scarce and abundant unlabeled data is available, the concept of positive-unlabeled learning can be utilized to improve model training and performance. By incorporating domain-unlabeled data and leveraging PU learning techniques, semi-supervised learning algorithms can effectively utilize the unlabeled data to enhance model generalization and accuracy. Furthermore, in transfer learning scenarios, where knowledge transfer between related domains is essential, the principles of domain identification and adaptation explored in the PUORL framework can be leveraged to facilitate domain alignment and transfer of knowledge across different domains. By applying the strategies and methodologies developed in the context of offline RL to these broader machine learning domains, researchers and practitioners can enhance the efficiency and effectiveness of learning algorithms in various real-world applications.