Offline reinforcement learning (RL) provides a promising solution to learning an agent fully relying on a data-driven paradigm. However, constrained by the limited quality of the offline dataset, its performance is often sub-optimal. Therefore, it is desired to further finetune the agent via extra online interactions before deployment. Unfortunately, offline-to-online RL can be challenging due to two main challenges: constrained exploratory behavior and state-action distribution shift. To this end, we propose a Simple Unified uNcertainty-Guided (SUNG) framework, which naturally unifies the solution to both challenges with the tool of uncertainty. Specifically, SUNG quantifies uncertainty via a VAE-based state-action visitation density estimator. To facilitate efficient exploration, SUNG presents a practical optimistic exploration strategy to select informative actions with both high value and high uncertainty. Moreover, SUNG develops an adaptive exploitation method by applying conservative offline RL objectives to high-uncertainty samples and standard online RL objectives to low-uncertainty samples to smoothly bridge offline and online stages. SUNG achieves state-of-the-art online finetuning performance when combined with different offline RL methods, across various environments and datasets in D4RL benchmark.

翻译：离线强化学习提供了一种完全依赖数据驱动范式来学习智能体的有前景方案。然而，由于离线数据集质量的限制，其性能往往次优，因此需要在部署前通过额外的在线交互对智能体进行微调。不幸的是，离线到在线强化学习面临两大挑战：约束性探索行为和状态-动作分布偏移。为此，我们提出了一种简单的统一不确定性引导框架，该框架利用不确定性工具自然地将两个挑战的解决方案统一起来。具体而言，该框架通过基于变分自编码器的状态-动作访问密度估计器量化不确定性。为促进高效探索，该框架提出了一种实用乐观探索策略，选择兼具高价值与高不确定性的信息性动作。此外，该框架开发了一种自适应利用方法：对高不确定性样本应用保守离线强化学习目标，对低不确定性样本应用标准在线强化学习目标，从而平滑衔接离线和在线阶段。在D4RL基准测试的不同环境与数据集中，该框架与多种离线强化学习方法结合时均实现了最先进的在线微调性能。