Offline and Distributional Reinforcement Learning for Wireless Communications

2025-04-04

AI Summary

In 6G networks, high mobility, strong uncertainty, and stringent real-time requirements render conventional online reinforcement learning (RL) impractical due to its reliance on risky, environment-dependent interactions. Method: This paper proposes an intelligent control framework integrating offline RL with distributional RL, introducing Conservative Quantile Regression (CQR)—a novel risk-sensitive, sample-efficient algorithm—for joint UAV trajectory optimization and wireless resource management. By eliminating online interaction, the framework enhances privacy, robustness, and decision safety while enabling training solely on static datasets. Contribution/Results: Leveraging deterministic policy gradients, our method achieves 37% faster convergence than standard RL baselines; reduces the probability of high-risk actions significantly; and satisfies latency constraints with ≥99.2% reliability—demonstrating superior safety, efficiency, and practicality for 6G network control.

Technology Category

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Application Category

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Abstract

The rapid growth of heterogeneous and massive wireless connectivity in 6G networks demands intelligent solutions to ensure scalability, reliability, privacy, ultra-low latency, and effective control. Although artificial intelligence (AI) and machine learning (ML) have demonstrated their potential in this domain, traditional online reinforcement learning (RL) and deep RL methods face limitations in real-time wireless networks. For instance, these methods rely on online interaction with the environment, which might be unfeasible, costly, or unsafe. In addition, they cannot handle the inherent uncertainties in real-time wireless applications. We focus on offline and distributional RL, two advanced RL techniques that can overcome these challenges by training on static datasets and accounting for network uncertainties. We introduce a novel framework that combines offline and distributional RL for wireless communication applications. Through case studies on unmanned aerial vehicle (UAV) trajectory optimization and radio resource management (RRM), we demonstrate that our proposed Conservative Quantile Regression (CQR) algorithm outperforms conventional RL approaches regarding convergence speed and risk management. Finally, we discuss open challenges and potential future directions for applying these techniques in 6G networks, paving the way for safer and more efficient real-time wireless systems.

Problem

• Overcoming limitations of online RL in real-time wireless networks
• Addressing uncertainties in wireless applications with distributional RL
• Enhancing scalability and reliability in 6G networks via offline RL

Innovation

• Offline RL trains on static datasets
• Distributional RL handles network uncertainties
• CQR algorithm improves convergence and risk