Multi-agent reinforcement learning for a distributed multi-channel access game

Abstract

In this work, we model multi-user distributed channel access as a game with U channels and N users, and propose the Multi-Agent Thompson Sampling (MA-TS) algorithm. It uses Bayes’ theorem to dynamically optimize action selection. This optimization aims to maximize throughput. We derive the algorithm’s computational complexity as O(TNUNmax2). Simulations show that MA-TS converges to a pure strategy Nash equilibrium (PNE) and outperforms existing methods in average throughput.