Joint Movement and User Association of UAV-BS for Indoor User Service: A Multi-Agent Deep Reinforcement Learning Approach

Abstract

This paper investigates the joint optimization of movement and user association for multiple UAV base stations (UAV-BSs) to provide emergency services to indoor users. In this paper, we propose a multi-agent deep Q-network (MADQN) reinforcement learning algorithm to address this problem. Moreover, we apply a self-attention mechanism to achieve the objective more effectively. Simulation results demonstrate that the proposed algorithm outperforms conventional algorithms, such as random action and MADQN without a self-attention mechanism.