  Combining offline and online reinforcement learning (RL) techniques is indeed
crucial for achieving efficient and safe learning where data acquisition is
expensive. Existing methods replay offline data directly in the online phase,
resulting in a significant challenge of data distribution shift and
subsequently causing inefficiency in online fine-tuning. To address this issue,
we introduce an innovative approach, \textbf{E}nergy-guided \textbf{DI}ffusion
\textbf{S}ampling (EDIS), which utilizes a diffusion model to extract prior
knowledge from the offline dataset and employs energy functions to distill this
knowledge for enhanced data generation in the online phase. The theoretical
analysis demonstrates that EDIS exhibits reduced suboptimality compared to
solely utilizing online data or directly reusing offline data. EDIS is a
plug-in approach and can be combined with existing methods in offline-to-online
RL setting. By implementing EDIS to off-the-shelf methods Cal-QL and IQL, we
observe a notable 20% average improvement in empirical performance on MuJoCo,
AntMaze, and Adroit environments. Code is available at
\url{https://github.com/liuxhym/EDIS}.
