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arXiv · 2201.10070

MOORe: Model-based Offline-to-Online Reinforcement Learning

Yihuan Mao·Chao Wang·Bin Wang·Chongjie Zhang
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Abstract

With the success of offline reinforcement learning (RL), offline trained RL policies have the potential to be further improved when deployed online. A smooth transfer of the policy matters in safe real-world deployment. Besides, fast adaptation of the policy plays a vital role in practical online performance improvement. To tackle these challenges, we propose a simple yet efficient algorithm, Model-based Offline-to-Online Reinforcement learning (MOORe), which employs a prioritized sampling scheme that can dynamically adjust the offline and online data for smooth and efficient online adaptation of the policy. We provide a theoretical foundation for our algorithms design. Experiment results on the D4RL benchmark show that our algorithm smoothly transfers from offline to online stages while enabling sample-efficient online adaption, and also significantly outperforms existing methods.

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Yihuan Mao, Chao Wang, Bin Wang, Chongjie Zhang. 2022-01-25. MOORe: Model-based Offline-to-Online Reinforcement Learning. https://arxiv.org/abs/2201.10070

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