  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.

Networking and Internet Architecture;Machine Learning;Systems and Control;Systems and Control