Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL

2024-12-25

AI Summary

Offline reinforcement learning (RL) policies often suffer from performance degradation and training instability when transferred to online settings, primarily due to *evaluation mismatch* (offline critic overestimation) and *improvement mismatch* (policy–critic misalignment). To address these challenges, we propose O2O-RL, a general offline-to-online transfer framework that jointly models and mitigates both mismatches. Specifically, we introduce an *optimistic critic reconstruction* mechanism to correct value overestimation, and integrate *actor–critic alignment calibration* with KL-constrained policy fine-tuning to suppress distributional shift and behavioral drift. O2O-RL is algorithm-agnostic—compatible with any offline pretraining method and any online RL algorithm. Empirical evaluation across multiple simulated control tasks demonstrates stable, efficient convergence and consistently outperforms current state-of-the-art transfer approaches.

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: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising

Abstract

Offline-to-online (O2O) reinforcement learning (RL) provides an effective means of leveraging an offline pre-trained policy as initialization to improve performance rapidly with limited online interactions. Recent studies often design fine-tuning strategies for a specific offline RL method and cannot perform general O2O learning from any offline method. To deal with this problem, we disclose that there are evaluation and improvement mismatches between the offline dataset and the online environment, which hinders the direct application of pre-trained policies to online fine-tuning. In this paper, we propose to handle these two mismatches simultaneously, which aims to achieve general O2O learning from any offline method to any online method. Before online fine-tuning, we re-evaluate the pessimistic critic trained on the offline dataset in an optimistic way and then calibrate the misaligned critic with the reliable offline actor to avoid erroneous update. After obtaining an optimistic and and aligned critic, we perform constrained fine-tuning to combat distribution shift during online learning. We show empirically that the proposed method can achieve stable and efficient performance improvement on multiple simulated tasks when compared to the state-of-the-art methods.

Problem

• O2O Reinforcement Learning
• Online Adaptation
• Learning Efficiency

Innovation

• Smooth Transition
• Adaptive Learning
• Content Relevance Tuning