Unmanned aerial vehicle (UAV)-based wireless networks have received increasing research interest in recent years and are gradually being utilized in various aspects of our society. The growing complexity of UAV applications such as disaster management, plant protection, and environment monitoring, has resulted in escalating and stringent requirements for UAV networks that a single UAV cannot fulfill. To address this, multi-UAV wireless networks (MUWNs) have emerged, offering enhanced resource-carrying capacity and enabling collaborative mission completion by multiple UAVs. However, the effective operation of MUWNs necessitates a higher level of autonomy and intelligence, particularly in decision-making and multi-objective optimization under diverse environmental conditions. Reinforcement Learning (RL), an intelligent and goal-oriented decision-making approach, has emerged as a promising solution for addressing the intricate tasks associated with MUWNs. As one may notice, the literature still lacks a comprehensive survey of recent advancements in RL-based MUWNs. Thus, this paper aims to bridge this gap by providing a comprehensive review of RL-based approaches in the context of autonomous MUWNs. We present an informative overview of RL and demonstrate its application within the framework of MUWNs. Specifically, we summarize various applications of RL in MUWNs, including data access, sensing and collection, resource allocation for wireless connectivity, UAV-assisted mobile edge computing, localization, trajectory planning, and network security. Furthermore, we identify and discuss several open challenges based on the insights gained from our review.

中文翻译：

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走向自主多无人机无线网络：基于强化学习的方法的调查

近年来，基于无人机（UAV）的无线网络受到越来越多的研究兴趣，并逐渐应用于社会的各个方面。灾害管理、植保、环境监测等无人机应用日益复杂，对无人机网络的要求不断升级和严格，单台无人机无法满足。为了解决这个问题，多无人机无线网络（MUWN）应运而生，提供增强的资源承载能力并支持多无人机协作完成任务。然而，MUWN的有效运行需要更高水平的自主性和智能化，特别是在不同环境条件下的决策和多目标优化方面。强化学习 (RL) 是一种智能且以目标为导向的决策方法，已成为解决与 MUWN 相关的复杂任务的有前景的解决方案。人们可能会注意到，文献仍然缺乏对基于强化学习的 MUWN 最新进展的全面调查。因此，本文旨在通过对自主 MUWN 背景下基于强化学习的方法进行全面回顾来弥合这一差距。我们对 RL 进行了内容丰富的概述，并展示了其在 MUWN 框架内的应用。具体来说，我们总结了 RL 在 MUWN 中的各种应用，包括数据访问、感知和收集、无线连接的资源分配、无人机辅助的移动边缘计算、定位、轨迹规划和网络安全。此外，我们根据我们的审查获得的见解确定并讨论了几个开放的挑战。