Dynamic Channel Access via Meta-Reinforcement Learning

TL;DR

This paper introduces a meta-reinforcement learning framework using MAML to enable rapid adaptation for dynamic channel access in wireless networks, reducing training data needs and improving robustness to environmental changes.

Contribution

It proposes a novel meta-DRL approach with MAML for efficient, adaptable channel selection in dynamic wireless environments, addressing data efficiency and environmental variability.

Findings

Meta-DRL with MAML accelerates adaptation to new tasks.

Significant performance improvements in simulations.

Reduces training data requirements for channel access.

Abstract

In this paper, we address the channel access problem in a dynamic wireless environment via meta-reinforcement learning. Spectrum is a scarce resource in wireless communications, especially with the dramatic increase in the number of devices in networks. Recently, inspired by the success of deep reinforcement learning (DRL), extensive studies have been conducted in addressing wireless resource allocation problems via DRL. However, training DRL algorithms usually requires a massive amount of data collected from the environment for each specific task and the well-trained model may fail if there is a small variation in the environment. In this work, in order to address these challenges, we propose a meta-DRL framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). In the proposed framework, we train a common initialization for similar channel selection tasks. From the initialization, we show that only a few gradient descents are required for adapting to different tasks drawn from the same distribution. We demonstrate the performance improvements via simulation results.