How to Leverage Unlabeled Data in Offline Reinforcement Learning (2202.01741v4)

Abstract: Offline reinforcement learning (RL) can learn control policies from static datasets but, like standard RL methods, it requires reward annotations for every transition. In many cases, labeling large datasets with rewards may be costly, especially if those rewards must be provided by human labelers, while collecting diverse unlabeled data might be comparatively inexpensive. How can we best leverage such unlabeled data in offline RL? One natural solution is to learn a reward function from the labeled data and use it to label the unlabeled data. In this paper, we find that, perhaps surprisingly, a much simpler method that simply applies zero rewards to unlabeled data leads to effective data sharing both in theory and in practice, without learning any reward model at all. While this approach might seem strange (and incorrect) at first, we provide extensive theoretical and empirical analysis that illustrates how it trades off reward bias, sample complexity and distributional shift, often leading to good results. We characterize conditions under which this simple strategy is effective, and further show that extending it with a simple reweighting approach can further alleviate the bias introduced by using incorrect reward labels. Our empirical evaluation confirms these findings in simulated robotic locomotion, navigation, and manipulation settings.

Leveraging Unlabeled Data in Offline Reinforcement Learning: A Simplified Approach and Its Implications

The paper "How to Leverage Unlabeled Data in Offline Reinforcement Learning" addresses a compelling issue in the paradigm of offline reinforcement learning (RL). Offline RL has gained substantial attention due to its ability to learn control policies from static datasets. However, as existing methodologies predominantly depend on annotated reward data, there are significant hurdles when dealing with cost-prohibitive scenarios where reward labeling is required for extensive datasets. This paper proposes a counterintuitive yet insightful approach to tackle the challenge: applying zero rewards to unlabeled data.

Summary of the Approach

The primary contribution of the paper lies in its exploration of an unconventional method termed as "unlabeled data sharing" (UDS), which applies zero rewards to unlabeled transitions without invoking any reward inference model. This approach stands in contrast to previous methods that either infer rewards using learned classifiers or employ reward learning via inverse reinforcement learning (IRL).

The authors support their methodology with both theoretical analyses and empirical evidence. They propose that despite the inherent bias introduced by assigning incorrect zero rewards, the approach improves policy performance by managing the trade-off involving reward bias, sample complexity, and distributional shifts. Theoretical insights pertained to conditions where the methodology effectively diminishes the potential negative impact of reward mislabeling.

Empirical Findings and Theoretical Implications

The findings from the analysis prove that UDS can outperform more sophisticated techniques like reward inferencing, especially when the dataset exhibits diverse and broad state coverage or when the labeled data is high-quality and narrow. UDS works well in scenarios where abundant unlabeled data is available, either circumventing or balancing out any reward mislabeling. Additionally, the efficacy is amplified when coupled with data reweighting strategies that further alleviate biases and enhance the distribution of data.

Empirically examined across simulated robotic tasks including locomotion and manipulation, the approach demonstrates robustness, indicating that naive strategies with appropriate execution and conditions could surpass more elaborate systems.

Impact and Future Directions

This paper not only challenges the foundational blocks of existing offline RL methods reliant on reward models but also reshapes considerations for practical applications where reward labeling is either expensive or infeasible. It suggests that simplifying certain parts of RL pipelines—such as rewarding schemes—can yield tangible benefits without exhaustive model designs.

The potential for future work is equally significant, with an emphasis on adaptive reweighting strategies and exploring synergies between labeled and unlabeled data dynamics. Further exploration into hybrid strategies that incorporate domain-specific knowledge and RL policy adaptation could also explore minimizing the innate reward bias of the proposed method.

In summary, by leveraging the surprisingly simple relabeling mechanism in specific conditions, this paper provides a pathway towards optimizing the application of offline RL systems in varied domains. Future development from this groundwork could considerably reduce the cost and complexity associated with deploying RL in real-world scenarios, potentially creating more adaptable, efficient, and scalable learning frameworks.