Overview
Offline-to-online reinforcement learning (RL) integrates offline dataset pretraining with online interaction fine-tuning. This paradigm, however, exhibits inconsistent empirical behavior, where specific online fine-tuning design choices may be effective in some contexts but fail in others.
Research Context
The research addresses the observed inconsistency in offline-to-online RL's empirical performance during online fine-tuning. The operational efficacy of online fine-tuning methods varies significantly depending on the setting. To address this, a framework was developed, guided by the stability–plasticity principle, which posits that effective fine-tuning must maintain the utility of a stronger offline prior while ensuring sufficient plasticity. The offline prior can be either the pretrained policy or the initial offline dataset.
Approach
The proposed framework categorizes online fine-tuning into three distinct regimes, each requiring specific stability properties. The core idea is that the relative performance of the offline dataset and the pretrained policy influences the optimal fine-tuning strategy. The framework was evaluated through a large-scale empirical study to assess its predictive alignment with observed outcomes.
Findings
- The stability–plasticity principle provides a theoretical basis for understanding the empirical inconsistency of offline-to-online RL.
- Efficient online fine-tuning necessitates the preservation of the stronger offline prior's utility, which can derive from either the pretrained policy or the offline dataset itself.
- Simultaneously, adequate plasticity must be maintained during the fine-tuning process.
- The framework identifies three specific regimes for online fine-tuning, with each regime characterized by unique stability requirements.
- An large-scale empirical study was conducted to validate the framework.
- The results of this study indicated a strong alignment with the framework’s predictions in 45 out of 63 tested cases.
- Only 3 instances showed an opposite mismatch compared to the framework's predictions.
Why This Matters
This work offers a framework that can guide design choices in offline-to-online RL. The guidance is based on the relative performance dynamics between the offline dataset and the pretrained policy, potentially leading to more consistent and effective fine-tuning strategies.