  Offline reinforcement learning (ORL) has gained attention as a means of
training reinforcement learning models using pre-collected static data. To
address the issue of limited data and improve downstream ORL performance,
recent work has attempted to expand the dataset's coverage through data
augmentation. However, most of these methods are tied to a specific policy
(policy-dependent), where the generated data can only guarantee to support the
current downstream ORL policy, limiting its usage scope on other downstream
policies. Moreover, the quality of synthetic data is often not well-controlled,
which limits the potential for further improving the downstream policy. To
tackle these issues, we propose \textbf{HI}gh-quality
\textbf{PO}licy-\textbf{DE}coupled~(HIPODE), a novel data augmentation method
for ORL. On the one hand, HIPODE generates high-quality synthetic data by
selecting states near the dataset distribution with potentially high value
among candidate states using the negative sampling technique. On the other
hand, HIPODE is policy-decoupled, thus can be used as a common plug-in method
for any downstream ORL process. We conduct experiments on the widely studied
TD3BC and CQL algorithms, and the results show that HIPODE outperforms the
state-of-the-art policy-decoupled data augmentation method and most prevalent
model-based ORL methods on D4RL benchmarks.
