Online Adaptable Offline RL With Guidance Model

Reinforcement learning (RL) has emerged as a promising approach across various applications, yet its reliance on repeated trial-and-error learning to develop effective policies from scratch poses significant challenges for deployment in scenarios where interaction is costly or constrained. In this work, we investigate the offline-to-online RL paradigm, wherein policies are initially pretrained using offline historical datasets and subsequently fine-tuned with a limited amount of online interaction. Previous research has suggested that efficient offline pretraining is crucial for achieving optimal final performance. However, it is challenging to incorporate appropriate conservatism to prevent the overestimation of out-of-distribution (OOD) data while maintaining adaptability for online fine-tuning. To address these issues, we propose an effective offline RL algorithm that integrates a guidance model to introduce suitable conservatism and ensure seamless adaptability to online fine-tuning. Our rigorous theoretical analysis and extensive experimental evaluations demonstrate better performance of our novel algorithm, underscoring the critical role played by the guidance model in enhancing its efficacy.