A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning

• URL: http://arxiv.org/abs/2306.07541v2
• Date: Wed, 21 Feb 2024 03:07:23 GMT
• Title: A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
• Authors: Siyuan Guo, Yanchao Sun, Jifeng Hu, Sili Huang, Hechang Chen, Haiyin Piao, Lichao Sun, Yi Chang
• Abstract summary: offline-to-online reinforcement learning can be challenging due to constrained exploratory behavior and state-action distribution shift. We propose a Simple Unified uNcertainty-Guided (SUNG) framework, which unifies the solution to both challenges with the tool of uncertainty. SUNG achieves state-of-the-art online finetuning performance when combined with different offline RL methods.
• Score: 25.123237633748193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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