Offline-Online Reinforcement Learning for Linear Mixture MDPs

Researchers Zhongjun Zhang and Sean R. Sinclair have introduced a novel algorithm for reinforcement learning (RL) that intelligently combines offline and online learning phases, specifically designed for environments modeled as linear mixture Markov Decision Processes (MDPs). The core challenge they address is 'environment shift,' where data collected in an initial offline phase—potentially by an unknown policy and from a mismatched environment—must be integrated with real-time online interaction in a target environment. Their solution is an adaptive algorithm that provably decides how much to rely on the pre-collected data.

The algorithm's strength lies in its safety and efficiency guarantees. When the offline data has sufficient coverage of the target environment's state-action space or the shift between environments is small, the algorithm leverages this data to achieve provably better performance than purely online learning, with theoretical regret bounds quantifying the improvement. Conversely, if the offline data is uninformative or misleading, the algorithm safely disregards it, automatically reverting to match the performance of an online-only learner. This prevents the negative transfer that can plague simpler methods.

The 72-page paper, available on arXiv, establishes both upper and nearly matching lower regret bounds, providing a complete theoretical characterization of when offline data becomes beneficial. Numerical experiments further validate these findings, showing practical performance gains. This work provides a rigorous framework for a critical problem in real-world RL deployment, where collecting fresh online data is expensive or risky, but historical data may not perfectly align with the current task.