Offline and Distributional Reinforcement Learning for Wireless Communications

TL;DR

This work tackles the challenge of applying reinforcement learning in wireless networks where online interaction is costly or unsafe and environmental uncertainties hinder performance. It proposes a joint offline and distributional RL framework, centered on Conservative Quantile Regression (CQR), to train policies from static datasets while accounting for risk via return distributions. Through UAV trajectory optimization and radio resource management case studies, CQR achieves faster convergence and improved risk control compared to traditional online and offline baselines. The work highlights practical pathways for safer, scalable optimization in 6G and outlines open challenges and future directions, including hybrid online-offline training, scalability, and multi-agent extensions.

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.

Figures (5)

• Figure 1: Illustration of a $6$G network with multiple reinforcement learning applications. This includes smart factories, smart agriculture, unmanned aerial vehicle networks, autonomous and connected vehicles, and radio resource management.
• Figure 2: Reinforcement learning evolution towards offline reinforcement learning: (a) online reinforcement learning, (b) off-policy deep reinforcement learning, (c) offline reinforcement learning, and (d) distributional reinforcement learning. In (a) and (b), the agent can interact with the environment online while optimizing its policy. In (c), the agent can only access a static offline dataset collected previously using behavior policy. In (d), the agent utilizes the return distribution instead of the expected return.
• Figure 3: Average test return (normalized by $1000$) over $100$ unique test episodes as a function of the number of training epochs.
• Figure 4: The percentage of violations (the UAV enters the risk region) for different RL schemes averaged over $100$ unique test episodes.
• Figure 5: Average test Rscore over $100$ unique test episodes as a function of the number of training epochs.