Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation

Abstract:We study value adaptation in offline-to-online reinforcement learning under general function approximation. Starting from an imperfect offline pretrained $Q$-function, the learner aims to adapt it to the target environment using only a limited amount of online interaction. We first characterize the difficulty of this setting by establishing a minimax lower bound, showing that even when the pretrained $Q$-function is close to optimal $Q^\star$, online adaptation can be no more efficient than pure online RL on certain hard instances. On the positive side, under a novel structural condition on the offline-pretrained value functions, we propose O2O-LSVI, an adaptation algorithm with problem-dependent sample complexity that provably improves over pure online RL. Finally, we complement our theory with neural-network experiments that demonstrate the practical effectiveness of the proposed method.

Machine Learning (cs.LG)

arXiv:2604.13966 [cs.LG]

(or arXiv:2604.13966v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2604.13966 Focus to learn more arXiv-issued DOI via DataCite

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