From Static to Dynamic: Enhancing Offline-to-Online Reinforcement Learning via Energy-Guided Diffusion Stratification

2025-11-05

AI Summary

Offline-to-online reinforcement learning suffers from policy adaptation difficulties due to behavioral policy distribution shift, while existing methods lack explicit modeling of the intrinsic distributional structure of offline data. To address this, we propose Energy-guided Diffusion Hierarchical learning (EDH), the first framework integrating diffusion models with energy-based functions: a diffusion model learns the prior distribution of offline data, while an energy function dynamically partitions samples into “offline-like” and “online-like” subsets; hierarchical policy updates and adaptive mixed training are then performed via KL-divergence minimization. EDH significantly mitigates distributional shift and enhances policy transfer stability. On the D4RL benchmark, EDH outperforms state-of-the-art offline-to-online methods. Furthermore, when integrated with Cal-QL and IQL, EDH improves online adaptability and training robustness.

Technology Category

• Machine Learning: Online Learning & Bandits
• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Addresses distributional shifts between offline and online reinforcement learning policies
• Proposes energy-guided diffusion to stratify samples for adaptive learning strategies
• Enhances policy imitation and stability during offline-to-online transition phases

Innovation

• Uses diffusion model to learn offline dataset knowledge
• Employs energy functions to generate offline-like actions
• Stratifies samples using KL divergence for hybrid training