  Recent advance in deep offline reinforcement learning (RL) has made it
possible to train strong robotic agents from offline datasets. However,
depending on the quality of the trained agents and the application being
considered, it is often desirable to fine-tune such agents via further online
interactions. In this paper, we observe that state-action distribution shift
may lead to severe bootstrap error during fine-tuning, which destroys the good
initial policy obtained via offline RL. To address this issue, we first propose
a balanced replay scheme that prioritizes samples encountered online while also
encouraging the use of near-on-policy samples from the offline dataset.
Furthermore, we leverage multiple Q-functions trained pessimistically offline,
thereby preventing overoptimism concerning unfamiliar actions at novel states
during the initial training phase. We show that the proposed method improves
sample-efficiency and final performance of the fine-tuned robotic agents on
various locomotion and manipulation tasks. Our code is available at:
https://github.com/shlee94/Off2OnRL.
