Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation
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-d...
| Authors | Shangzhe Li, Weitong Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 15 April 2026 |
| Last updated at source | 7 October 2026 |
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