ORL-AUDITOR: Dataset Auditing in Offline Deep Reinforcement Learning

Understand

Data is a critical asset in AI, as high-quality datasets can significantly improve the performance of machine learning models.

• In safety-critical domains such as autonomous vehicles, offline deep reinforcement learning (offline DRL) is frequently used to train models on pre-collected datasets, as opposed to training these models by interacting with the real-world environment as the online DRL.
• To support the development of these models, many institutions make datasets publicly available with opensource licenses, but these datasets are at risk of potential misuse or infringement.
• Injecting watermarks to the dataset may protect the intellectual property of the data, but it cannot handle datasets that have already been published and is infeasible to be altered afterward.