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SAMG: Offline-to-Online Reinforcement Learning via State-Action-Conditional Offline Model Guidance

Liyu Zhang, Haochi Wu, Xu Wan, Quan Kong, Ruilong Deng, Mingyang Sun

TL;DR

SAMG tackles inefficiency in offline-to-online RL by freezing a pre-trained offline critic and guiding online refinement through a state-action-conditional coefficient that weighs offline guidance. The method integrates a frozen Q^{off} with the online Q via a data-driven coefficient p(s,a) learned from a conditional VAE, enabling 100% online data usage and avoiding reliance on offline data during online learning. The authors provide contraction-based theoretical analysis showing convergence to the online optimal policy and faster convergence on in-distribution samples, plus empirical wins on D4RL benchmarks. Limitations arise when offline coverage is narrow, suggesting adaptive data-sharing or OOD strategies as future work.

Abstract

Offline-to-online (O2O) reinforcement learning (RL) pre-trains models on offline data and refines policies through online fine-tuning. However, existing O2O RL algorithms typically require maintaining the tedious offline datasets to mitigate the effects of out-of-distribution (OOD) data, which significantly limits their efficiency in exploiting online samples. To address this deficiency, we introduce a new paradigm for O2O RL called State-Action-Conditional Offline \Model Guidance (SAMG). It freezes the pre-trained offline critic to provide compact offline understanding for each state-action sample, thus eliminating the need for retraining on offline data. The frozen offline critic is incorporated with the online target critic weighted by a state-action-adaptive coefficient. This coefficient aims to capture the offline degree of samples at the state-action level, and is updated adaptively during training. In practice, SAMG could be easily integrated with Q-function-based algorithms. Theoretical analysis shows good optimality and lower estimation error. Empirically, SAMG outperforms state-of-the-art O2O RL algorithms on the D4RL benchmark.

SAMG: Offline-to-Online Reinforcement Learning via State-Action-Conditional Offline Model Guidance

TL;DR

SAMG tackles inefficiency in offline-to-online RL by freezing a pre-trained offline critic and guiding online refinement through a state-action-conditional coefficient that weighs offline guidance. The method integrates a frozen Q^{off} with the online Q via a data-driven coefficient p(s,a) learned from a conditional VAE, enabling 100% online data usage and avoiding reliance on offline data during online learning. The authors provide contraction-based theoretical analysis showing convergence to the online optimal policy and faster convergence on in-distribution samples, plus empirical wins on D4RL benchmarks. Limitations arise when offline coverage is narrow, suggesting adaptive data-sharing or OOD strategies as future work.

Abstract

Offline-to-online (O2O) reinforcement learning (RL) pre-trains models on offline data and refines policies through online fine-tuning. However, existing O2O RL algorithms typically require maintaining the tedious offline datasets to mitigate the effects of out-of-distribution (OOD) data, which significantly limits their efficiency in exploiting online samples. To address this deficiency, we introduce a new paradigm for O2O RL called State-Action-Conditional Offline \Model Guidance (SAMG). It freezes the pre-trained offline critic to provide compact offline understanding for each state-action sample, thus eliminating the need for retraining on offline data. The frozen offline critic is incorporated with the online target critic weighted by a state-action-adaptive coefficient. This coefficient aims to capture the offline degree of samples at the state-action level, and is updated adaptively during training. In practice, SAMG could be easily integrated with Q-function-based algorithms. Theoretical analysis shows good optimality and lower estimation error. Empirically, SAMG outperforms state-of-the-art O2O RL algorithms on the D4RL benchmark.

Paper Structure

This paper contains 38 sections, 4 theorems, 34 equations, 7 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. SAMG: Methodology
  4. Offline-model-guidance Paradigm
  5. State-action-conditional Coefficient
  6. Coefficient Generation and Adaptive Updates
  7. Analysis of SAMG
  8. Intuitive analysis of SAMG
  9. Theoretical analysis of SAMG
  10. Experimental Results
  11. Empirical Results
  12. Ablation Analysis of the Coefficient
  13. Sensitivity Analysis of Probability Threshold
  14. Further Comparisons with Hybrid RL Algorithms
  15. Does SAMG Rely on Offline Data Coverage?
  16. ...and 23 more sections

Key Result

Theorem 4.1

For a given policy $\pi$, by the TD updating paradigm, $Q_k(s, a)$ of SAMG converges almost surely to $Q^{\pi}(s, a)$ as $k\to \infty$ for all $s \in \mathcal{S}$ and $a \in \mathcal{A}$ if $\sum_k \alpha_k(s, a)=\infty$ and $\sum_k \alpha_k^2(s, a) < \infty$ for all $s \in \mathcal{S}$ and $a \in \

Figures (7)

  • Figure 1: Compared with the vanilla O2O framework (a), the proposed SAMG (b) uses the frozen offline RL model with an adaptive distribution-aware model to achieve online fine-tuning with 100% online sample rates.
  • Figure 2: Architecture of SAMG. This figure illustrates the structure of SAMG, highlighting the transition from offline pre-training to online fine-tuning. It outlines key components, including offline critic, VAE model, offline-guidance technique, and adaptive coefficient.
  • Figure 3: Ablation analysis of the state-action-conditional coefficient.
  • Figure 4: Sensitivity test for the coefficient threshold $p^{vae}_{m}$.
  • Figure 5: Average improvement of the normalized score over the offline data coverage.
  • ...and 2 more figures

Theorems & Definitions (4)

  • Theorem 4.1: Convergence property of SAMG
  • Theorem 4.2: Convergence speed of SAMG
  • Theorem 1.1: Contraction mapping theorem
  • Theorem 1.2: Dvoretzky's Throrem