From Static to Dynamic: Enhancing Offline-to-Online Reinforcement Learning via Energy-Guided Diffusion Stratification
This addresses a critical problem in reinforcement learning for improving adaptability and stability when transitioning from offline to online learning, though it appears incremental as it builds on existing methods.
The paper tackles the challenge of distributional shifts in offline-to-online reinforcement learning by proposing StratDiff, a method that uses diffusion models and energy-based functions to stratify training samples, which when integrated with existing methods like Cal-QL and IQL, significantly outperforms prior approaches on D4RL benchmarks.
Transitioning from offline to online reinforcement learning (RL) poses critical challenges due to distributional shifts between the fixed behavior policy in the offline dataset and the evolving policy during online learning. Although this issue is widely recognized, few methods attempt to explicitly assess or utilize the distributional structure of the offline data itself, leaving a research gap in adapting learning strategies to different types of samples. To address this challenge, we propose an innovative method, Energy-Guided Diffusion Stratification (StratDiff), which facilitates smoother transitions in offline-to-online RL. StratDiff deploys a diffusion model to learn prior knowledge from the offline dataset. It then refines this knowledge through energy-based functions to improve policy imitation and generate offline-like actions during online fine-tuning. The KL divergence between the generated action and the corresponding sampled action is computed for each sample and used to stratify the training batch into offline-like and online-like subsets. Offline-like samples are updated using offline objectives, while online-like samples follow online learning strategies. We demonstrate the effectiveness of StratDiff by integrating it with off-the-shelf methods Cal-QL and IQL. Extensive empirical evaluations on D4RL benchmarks show that StratDiff significantly outperforms existing methods, achieving enhanced adaptability and more stable performance across diverse RL settings.