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

2026-04-15

AI Summary

This work addresses the challenge of efficiently adapting an offline pre-trained Q-function to a target environment with only limited online interactions. Focusing on value transfer from offline to online reinforcement learning, the paper proposes the O2O-LSVI algorithm, which operates within a general function approximation framework based on least-squares value iteration. The method incorporates theoretically motivated structural conditions and establishes, for the first time, a minimax lower bound under this setting. O2O-LSVI achieves instance-dependent sample complexity that is provably superior to that of purely online reinforcement learning. Empirical evaluations using neural networks demonstrate its effectiveness in practical tasks, validating both its theoretical advantages and real-world applicability.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• offline-to-online reinforcement learning
• value adaptation
• function approximation
• sample complexity
• pretrained Q-function

Innovation

• offline-to-online RL
• value adaptation
• general function approximation
• minimax lower bound
• O2O-LSVI