Uncertainty-Aware Rank-One MIMO Q Network Framework for Accelerated Offline Reinforcement Learning

2026-02-23

AI Summary

This work addresses the challenges posed by out-of-distribution (OOD) data in offline reinforcement learning, particularly the extrapolation error and the limitations of existing approaches—such as excessive conservatism, inaccurate modeling, and high computational overhead—when leveraging OOD samples. To overcome these issues, the paper proposes a novel Q-network framework that integrates uncertainty-aware learning with a Rank-One MIMO architecture. By explicitly quantifying data uncertainty and incorporating it into the training loss, the method optimizes policies to maximize the lower confidence bound of the Q-function, thereby ensuring stable learning while effectively utilizing OOD data. The approach innovatively combines low-rank MIMO structure with uncertainty modeling, achieving performance comparable to ensemble methods at computational and memory costs close to those of a single network. Empirical results demonstrate state-of-the-art performance on the D4RL benchmark suite.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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, aiming to train a policy that maximizes the lower confidence bound of the corresponding Q-function. Furthermore, a Rank-One MIMO architecture is introduced to model the uncertainty-aware Q-function, \TP{offering the same ability for uncertainty quantification as an ensemble of networks but with a cost nearly equivalent to that of a single network}. Consequently, this framework strikes a harmonious balance between precision, speed, and memory efficiency, culminating in improved overall performance. Extensive experimentation on the D4RL benchmark demonstrates that the framework attains state-of-the-art performance while remaining computationally efficient. By incorporating the concept of uncertainty quantification, our framework offers a promising avenue to alleviate extrapolation errors and enhance the efficiency of offline RL.

Problem

• offline reinforcement learning
• out-of-distribution
• extrapolation error
• uncertainty quantification
• OOD data

Innovation

• Uncertainty Quantification
• Rank-One MIMO
• Offline Reinforcement Learning
• Out-of-Distribution Data
• Efficient Ensemble