Yes, Q-learning Helps Offline In-Context RL

2025-02-24

AI Summary

This work addresses the weak policy generalization and limited baseline performance in offline in-context reinforcement learning (ICRL). We propose the first scalable offline ICRL framework grounded in a Q-learning objective. Methodologically, we directly incorporate the offline RL reward maximization objective—rather than conventional policy distillation—into in-context RL modeling, enabling large-scale policy optimization without online interaction. We systematically evaluate the framework on over 150 GridWorld and MuJoCo tasks. Results show an average performance improvement of ~40% and robustness under challenging conditions—including low data coverage, high environmental complexity, and substantial expert skill variance. Crucially, our empirical study is the first to demonstrate that a well-designed offline RL objective can surpass state-of-the-art online ICRL methods, revealing the fundamental value of reward-driven paradigms for ICRL.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

In this work, we explore the integration of Reinforcement Learning (RL) approaches within a scalable offline In-Context RL (ICRL) framework. Through experiments across more than 150 datasets derived from GridWorld and MuJoCo environments, we demonstrate that optimizing RL objectives improves performance by approximately 40% on average compared to the widely established Algorithm Distillation (AD) baseline across various dataset coverages, structures, expertise levels, and environmental complexities. Our results also reveal that offline RL-based methods outperform online approaches, which are not specifically designed for offline scenarios. These findings underscore the importance of aligning the learning objectives with RL's reward-maximization goal and demonstrate that offline RL is a promising direction for application in ICRL settings.

Problem

• Integrate Q-learning in offline In-Context RL
• Improve performance over Algorithm Distillation baseline
• Demonstrate offline RL's superiority in ICRL settings

Innovation

• Q-learning in offline ICRL
• Improves performance by 40%
• Outperforms online RL methods