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

Lipeng Zu, Hansong Zhou, Xiaonan Zhang

TL;DR

Energy-Guided Diffusion Stratification (StratDiff) is proposed, which facilitates smoother transitions in offline-to-online RL, and significantly outperforms existing methods, achieving enhanced adaptability and more stable performance across diverse RL settings.

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.

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

TL;DR

Energy-Guided Diffusion Stratification (StratDiff) is proposed, which facilitates smoother transitions in offline-to-online RL, and significantly outperforms existing methods, achieving enhanced adaptability and more stable performance across diverse RL settings.

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.

Paper Structure

This paper contains 28 sections, 1 theorem, 14 equations, 11 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Offline-to-online Reinforcement Learning
  4. Calibrated Q-Learning
  5. Implicit Q-Learning
  6. Contrastive Energy Prediction
  7. StratDiff: Energy-Guided Diffusion Stratification
  8. Why Policy Approximation Matters in Fine-Tuning?
  9. Stratification with Energy Guidance
  10. Calibrated Q-Learning with StratDiff
  11. Implicit Q-Learning with StratDiff
  12. Experiments
  13. Enhanced Performance with Energy-Guided Stratification
  14. Comparison of Online Training Processes
  15. Ablation for Energy Function in StratDiff
  16. ...and 13 more sections

Key Result

Theorem 2.1

(Thm. 3.1 in lu2023contrastive) Denote $q_t(x_t) := \int q_t(x_t | x_0) q_0(x_0) dx_0$ and $p_t(x_t) := \int p_t(x_t | x_0) p_0(x_0) dx_0$ as the marginal distributions at time $t$, and define The corresponding score function can be decomposed as:

Figures (11)

  • Figure 1: Overview of the proposed StratDiff framework.
  • Figure 2: Similarity between the actions from models and those in the offline dataset.
  • Figure 3: Online training processes comparison across various tasks based on Cal-QL.
  • Figure 4: Ablation results for showing the performance drop when removing energy function.
  • Figure 5: Comparison of online training processes on AntMaze navigation tasks based on Cal-QL.
  • ...and 6 more figures

Theorems & Definitions (1)

  • Theorem 2.1