MOORL: A Framework for Integrating Offline-Online Reinforcement Learning

TL;DR

MOORL is a hybrid reinforcement learning framework that combines offline and online methods through a meta-policy, improving exploration, stability, and performance across diverse tasks with minimal additional complexity.

Contribution

Introduces MOORL, a novel hybrid offline-online RL framework utilizing a meta-policy for seamless adaptation, enhancing exploration and stability without added computational overhead.

Findings

Outperforms state-of-the-art offline and hybrid RL methods on benchmark tasks.

Demonstrates stable Q-function learning without extra complexity.

Achieves consistent improvements with minimal computational overhead.

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.