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arXiv · 2305.10282

Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning

Gen Li·Wenhao Zhan·Jason D. Lee·Yuejie Chi·Yuxin Chen
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Abstract

This paper studies tabular reinforcement learning (RL) in the hybrid setting, which assumes access to both an offline dataset and online interactions with the unknown environment. A central question boils down to how to efficiently utilize online data collection to strengthen and complement the offline dataset and enable effective policy fine-tuning. Leveraging recent advances in reward-agnostic exploration and model-based offline RL, we design a three-stage hybrid RL algorithm that beats the best of both worlds -- pure offline RL and pure online RL -- in terms of sample complexities. The proposed algorithm does not require any reward information during data collection. Our theory is developed based on a new notion called single-policy partial concentrability, which captures the trade-off between distribution mismatch and miscoverage and guides the interplay between offline and online data.

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Gen Li, Wenhao Zhan, Jason D. Lee, Yuejie Chi, Yuxin Chen. 2023-05-17. Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning. https://arxiv.org/abs/2305.10282

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