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[2602.19917] Uncertainty-Aware Rank-One MIMO Q Network Framework for Accelerated Offline Reinforcement Learning
Summary
This paper presents an Uncertainty-Aware Rank-One MIMO Q Network framework designed to enhance offline reinforcement learning by effectively managing out-of-distribution data and improving computational efficiency.
Why It Matters
The increasing interest in offline reinforcement learning necessitates innovative solutions to address extrapolation errors from out-of-distribution data. This framework offers a new approach that balances performance and efficiency, making it relevant for researchers and practitioners in machine learning and robotics.
Key Takeaways
- Introduces a novel framework for offline reinforcement learning that quantifies data uncertainty.
- Utilizes a Rank-One MIMO architecture to model uncertainty-aware Q-functions efficiently.
- Demonstrates state-of-the-art performance on the D4RL benchmark while maintaining computational efficiency.
- Addresses challenges related to extrapolation errors in offline RL.
- Offers a promising avenue for improving the efficiency of learning processes in AI applications.
Computer Science > Machine Learning arXiv:2602.19917 (cs) [Submitted on 23 Feb 2026] Title:Uncertainty-Aware Rank-One MIMO Q Network Framework for Accelerated Offline Reinforcement Learning Authors:Thanh Nguyen, Tung Luu, Tri Ton, Sungwoong Kim, Chang D. Yoo View a PDF of the paper titled Uncertainty-Aware Rank-One MIMO Q Network Framework for Accelerated Offline Reinforcement Learning, by Thanh Nguyen and 4 other authors View PDF HTML (experimental) Abstract:Offline reinforcement learning (RL) has garnered significant interest due to its safe and easily scalable paradigm. However, training under this paradigm presents its own challenge: the extrapolation error stemming from out-of-distribution (OOD) data. Existing methodologies have endeavored to address this issue through means like penalizing OOD Q-values or imposing similarity constraints on the learned policy and the behavior policy. Nonetheless, these approaches are often beset by limitations such as being overly conservative in utilizing OOD data, imprecise OOD data characterization, and significant computational overhead. To address these challenges, this paper introduces an Uncertainty-Aware Rank-One Multi-Input Multi-Output (MIMO) Q Network framework. The framework aims to enhance Offline Reinforcement Learning by fully leveraging the potential of OOD data while still ensuring efficiency in the learning process. Specifically, the framework quantifies data uncertainty and harnesses it in the training losses, aimin...
