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MOORL: A Framework for Integrating Offline-Online Reinforcement Learning

Gaurav Chaudhary, Wassim Uddin Mondal, Laxmidhar Behera

TL;DR

MOORL tackles core sample efficiency and exploration challenges in reinforcement learning by unifying offline data with online interaction through a meta-learning framework. It leverages a meta Q-function learned with Reptile-style updates atop a Soft Actor-Critic foundation, enabling distribution-robust learning without large ensembles or complex design choices. The approach provides theoretical bounds on the benefit of mixing offline and online data via distribution distance metrics and demonstrates strong, consistent performance across 28 tasks from D4RL and V-D4RL, including pixel-based settings, with stable Q-values. The results underscore MOORL’s practicality for real-world hybrid learning scenarios, offering a simple yet powerful alternative to more resource-intensive hybrid methods.

Abstract

Sample efficiency and exploration remain critical challenges in Deep Reinforcement Learning (DRL), particularly in complex domains. Offline RL, which enables agents to learn optimal policies from static, pre-collected datasets, has emerged as a promising alternative. However, offline RL is constrained by issues such as out-of-distribution (OOD) actions that limit policy performance and generalization. To overcome these limitations, we propose Meta Offline-Online Reinforcement Learning (MOORL), a hybrid framework that unifies offline and online RL for efficient and scalable learning. While previous hybrid methods rely on extensive design components and added computational complexity to utilize offline data effectively, MOORL introduces a meta-policy that seamlessly adapts across offline and online trajectories. This enables the agent to leverage offline data for robust initialization while utilizing online interactions to drive efficient exploration. Our theoretical analysis demonstrates that the hybrid approach enhances exploration by effectively combining the complementary strengths of offline and online data. Furthermore, we demonstrate that MOORL learns a stable Q-function without added complexity. Extensive experiments on 28 tasks from the D4RL and V-D4RL benchmarks validate its effectiveness, showing consistent improvements over state-of-the-art offline and hybrid RL baselines. With minimal computational overhead, MOORL achieves strong performance, underscoring its potential for practical applications in real-world scenarios.

MOORL: A Framework for Integrating Offline-Online Reinforcement Learning

TL;DR

MOORL tackles core sample efficiency and exploration challenges in reinforcement learning by unifying offline data with online interaction through a meta-learning framework. It leverages a meta Q-function learned with Reptile-style updates atop a Soft Actor-Critic foundation, enabling distribution-robust learning without large ensembles or complex design choices. The approach provides theoretical bounds on the benefit of mixing offline and online data via distribution distance metrics and demonstrates strong, consistent performance across 28 tasks from D4RL and V-D4RL, including pixel-based settings, with stable Q-values. The results underscore MOORL’s practicality for real-world hybrid learning scenarios, offering a simple yet powerful alternative to more resource-intensive hybrid methods.

Abstract

Sample efficiency and exploration remain critical challenges in Deep Reinforcement Learning (DRL), particularly in complex domains. Offline RL, which enables agents to learn optimal policies from static, pre-collected datasets, has emerged as a promising alternative. However, offline RL is constrained by issues such as out-of-distribution (OOD) actions that limit policy performance and generalization. To overcome these limitations, we propose Meta Offline-Online Reinforcement Learning (MOORL), a hybrid framework that unifies offline and online RL for efficient and scalable learning. While previous hybrid methods rely on extensive design components and added computational complexity to utilize offline data effectively, MOORL introduces a meta-policy that seamlessly adapts across offline and online trajectories. This enables the agent to leverage offline data for robust initialization while utilizing online interactions to drive efficient exploration. Our theoretical analysis demonstrates that the hybrid approach enhances exploration by effectively combining the complementary strengths of offline and online data. Furthermore, we demonstrate that MOORL learns a stable Q-function without added complexity. Extensive experiments on 28 tasks from the D4RL and V-D4RL benchmarks validate its effectiveness, showing consistent improvements over state-of-the-art offline and hybrid RL baselines. With minimal computational overhead, MOORL achieves strong performance, underscoring its potential for practical applications in real-world scenarios.

Paper Structure

This paper contains 36 sections, 18 equations, 3 figures, 7 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Markov Decision Process
  4. Meta-Reinforcement Learning
  5. Why Meta Learning?
  6. Integrating Online RL with Offline Data
  7. Expected Reward and its Performance Bound
  8. Problem Setup
  9. Performance Gain Expression
  10. Bounding the Performance Gain with Total Variation Distance
  11. MOORL: Meta Offline-Online RL
  12. Problem Definition
  13. Meta Q-Function Learning
  14. Experiments
  15. Evaluation on Offline D4RL Tasks
  16. ...and 21 more sections

Figures (3)

  • Figure 1: Learning curves showing the mean Q-values for the AntMaze-Medium task on Diverse and Play datasets. Figures \ref{['fig:ima']} and \ref{['fig:imb']} depict the performance of the MOORL and RLPD frameworks, respectively, demonstrating their learning stability and effectiveness across both datasets.
  • Figure 2: The plots show learning curves with normalized returns on the y-axis. Each curve represents the mean performance across 10 random seeds, with shaded regions indicating the standard deviation. The normalized return at each point is computed as the average over 10 evaluation episodes. All tasks are evaluated over 300K timesteps.
  • Figure 3: The plots illustrate the impact of the inner-loop adaptation step on learning. The y-axis represents the normalized return, while the x-axis denotes timesteps. The solid curves show the mean return across 10 random seeds, with shaded regions indicating the standard deviation. Each evaluation point is computed as the average return over 10 episodes. All tasks are evaluated over 300K timesteps.