  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. Other existing solutions, such as dataset inference and membership
inference, do not work well in the offline DRL scenario due to the diverse
model behavior characteristics and offline setting constraints. In this paper,
we advocate a new paradigm by leveraging the fact that cumulative rewards can
act as a unique identifier that distinguishes DRL models trained on a specific
dataset. To this end, we propose ORL-AUDITOR, which is the first
trajectory-level dataset auditing mechanism for offline RL scenarios. Our
experiments on multiple offline DRL models and tasks reveal the efficacy of
ORL-AUDITOR, with auditing accuracy over 95% and false positive rates less than
2.88%. We also provide valuable insights into the practical implementation of
ORL-AUDITOR by studying various parameter settings. Furthermore, we demonstrate
the auditing capability of ORL-AUDITOR on open-source datasets from Google and
DeepMind, highlighting its effectiveness in auditing published datasets.
ORL-AUDITOR is open-sourced at https://github.com/link-zju/ORL-Auditor.
