Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness

Xiaoyu Wen, Xudong Yu, Rui Yang, Haoyuan Chen, Chenjia Bai, Zhen Wang

TL;DR

The Robust Offlineto-Online (RO2O) algorithm is proposed, designed to enhance offline policies through uncertainty and smoothness, and to mitigate the performance drop in online adaptation.

Abstract

To obtain a near-optimal policy with fewer interactions in Reinforcement Learning (RL), a promising approach involves the combination of offline RL, which enhances sample efficiency by leveraging offline datasets, and online RL, which explores informative transitions by interacting with the environment. Offline-to-Online (O2O) RL provides a paradigm for improving an offline trained agent within limited online interactions. However, due to the significant distribution shift between online experiences and offline data, most offline RL algorithms suffer from performance drops and fail to achieve stable policy improvement in O2O adaptation. To address this problem, we propose the Robust Offline-to-Online (RO2O) algorithm, designed to enhance offline policies through uncertainty and smoothness, and to mitigate the performance drop in online adaptation. Specifically, RO2O incorporates Q-ensemble for uncertainty penalty and adversarial samples for policy and value smoothness, which enable RO2O to maintain a consistent learning procedure in online adaptation without requiring special changes to the learning objective. Theoretical analyses in linear MDPs demonstrate that the uncertainty and smoothness lead to a tighter optimality bound in O2O against distribution shift. Experimental results illustrate the superiority of RO2O in facilitating stable offline-to-online learning and achieving significant improvement with limited online interactions.

Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness

TL;DR

The Robust Offlineto-Online (RO2O) algorithm is proposed, designed to enhance offline policies through uncertainty and smoothness, and to mitigate the performance drop in online adaptation.

Abstract

To obtain a near-optimal policy with fewer interactions in Reinforcement Learning (RL), a promising approach involves the combination of offline RL, which enhances sample efficiency by leveraging offline datasets, and online RL, which explores informative transitions by interacting with the environment. Offline-to-Online (O2O) RL provides a paradigm for improving an offline trained agent within limited online interactions. However, due to the significant distribution shift between online experiences and offline data, most offline RL algorithms suffer from performance drops and fail to achieve stable policy improvement in O2O adaptation. To address this problem, we propose the Robust Offline-to-Online (RO2O) algorithm, designed to enhance offline policies through uncertainty and smoothness, and to mitigate the performance drop in online adaptation. Specifically, RO2O incorporates Q-ensemble for uncertainty penalty and adversarial samples for policy and value smoothness, which enable RO2O to maintain a consistent learning procedure in online adaptation without requiring special changes to the learning objective. Theoretical analyses in linear MDPs demonstrate that the uncertainty and smoothness lead to a tighter optimality bound in O2O against distribution shift. Experimental results illustrate the superiority of RO2O in facilitating stable offline-to-online learning and achieving significant improvement with limited online interactions.
Paper Structure (41 sections, 6 theorems, 44 equations, 7 figures, 5 tables, 1 algorithm)

This paper contains 41 sections, 6 theorems, 44 equations, 7 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Offline-to-Online RL
  4. Ensembles in RL
  5. Robustness in RL
  6. Preliminaries
  7. Offline-to-Online RL
  8. Linear MDPs
  9. Methodology
  10. Motivating Example
  11. Algorithm
  12. Ensemble-based Learning
  13. Robustness Regularization
  14. Smoothness Regularization
  15. Overestimation Penalty
  16. ...and 26 more sections

Key Result

Theorem 1

Assuming that the size of adversarial samples $\mathbb{B}_d(s^i_t,\epsilon)$ is sufficient and the Jacobian matrix of $\phi(s, a)$ has full rank, the smoothness constraint leads to smaller uncertainty for $\forall (s^{\star},a^{\star})\in {\mathcal{S}}\times{\mathcal{A}}$, as where the covariance matrices for these two LCB terms are $\widetilde{\Lambda}_t$ in Equation eq:covariance-rorl and $\wid

Figures (7)

  • Figure 1: Illustration for the motivating example. In the left panel, we visualize the trajectory distribution of two datasets, by mapping the trajectories into two-dimensional points using T-SNE ( ? ). The right panel presents the fine-tuning performance.
  • Figure 2: Overall framework of RO2O. RO2O employs the same off-policy RL algorithms during the offline-to-online training phase. By using OOD sampling, we incorporate $\mathcal{L}_{\rm{ood}}$ and $\mathcal{L}_{\rm{Qsmooth}}$ into the training process for the gradient update, while also calculating $\mathcal{L}_{\text{policy}}$ to constrain the policy $\pi_{\zeta}(\hat{s})$ as close as possible to the current policy $\pi_{\zeta}(s)$.
  • Figure 3: Fine-tuning performance curves of different methods across five seeds on MuJoCo locomotion tasks. The mean and standard deviation are shown by the solid lines and the shaded areas, respectively.
  • Figure 4: Fine-tuning performance curves of different methods across five seeds on Antmaze navigation tasks. The mean and standard deviation are shown by the solid lines and the shaded areas, respectively.
  • Figure 5: Offline (left column) and online performance (right column) when eliminating OOD penalty, policy smoothing, or $Q$-smoothing.
  • ...and 2 more figures

Theorems & Definitions (11)

  • Theorem 1
  • Theorem 2
  • Lemma 1
  • proof
  • Theorem 1: restate
  • proof
  • Lemma 2: Equivalence between LCB-penalty and Ensemble Uncertainty
  • proof
  • Theorem 2
  • proof
  • ...and 1 more