Offline and Distributional Reinforcement Learning for Radio Resource Management

TL;DR

This paper introduces an offline and distributional reinforcement learning approach for radio resource management, enabling training without environment interaction and effectively handling uncertainties, outperforming traditional and online RL methods.

Contribution

It presents a novel offline and distributional RL scheme for RRM that does not require environment interaction and accounts for uncertainties, surpassing existing methods.

Findings

Outperforms conventional resource management models

Surpasses online RL with a 10% gain

Effective in stochastic environments

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.