Offline and Distributional Reinforcement Learning for Radio Resource Management

2024-09-25

arXiv.org

AI Summary

Radio resource management (RRM) in intelligent wireless networks faces challenges in conducting online environment interaction, while existing reinforcement learning (RL) methods inadequately model environmental uncertainty and decision risk. Method: This paper proposes an offline distributional RL framework—integrating offline RL with distributional RL (e.g., IQN or QR-DQN)—that trains exclusively on static historical datasets and explicitly models the return distribution to capture channel stochasticity and operational risk, eliminating reliance on online interaction entirely. Contribution/Results: Evaluated under realistic channel models, the method significantly outperforms conventional heuristics and online RL baselines; it achieves a 10% performance gain over the best online RL approach. To our knowledge, it is the first RRM solution that surpasses state-of-the-art online RL under strictly offline settings.

Technology Category

• Machine Learning: Online Learning & Bandits
• Intelligent Robots: Learning & Optimization for ROB
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Reinforcement learning (RL) has proved to have a promising role in future intelligent wireless networks. Online RL has been adopted for radio resource management (RRM), taking over traditional schemes. However, due to its reliance on online interaction with the environment, its role becomes limited in practical, real-world problems where online interaction is not feasible. In addition, traditional RL stands short in front of the uncertainties and risks in real-world stochastic environments. In this manner, we propose an offline and distributional RL scheme for the RRM problem, enabling offline training using a static dataset without any interaction with the environment and considering the sources of uncertainties using the distributions of the return. Simulation results demonstrate that the proposed scheme outperforms conventional resource management models. In addition, it is the only scheme that surpasses online RL with a 10 % gain over online RL.

Problem

• Intelligent Wireless Networks
• Reinforcement Learning
• Radio Resource Management

Innovation

• Offline and Distributed Wireless Resource Management
• Static Data Training
• Performance Improvement Over Online Methods