Unmanned aerial vehicles (UAVs) are playing an increasingly pivotal role in modern communication networks,offering flexibility and enhanced coverage for a variety of applica-tions. However, UAV networks pose significant challenges due to their dynamic and distributed nature, particularly when dealing with tasks such as power allocation, channel assignment, caching,and task offloading. Traditional optimization techniques often struggle to handle the complexity and unpredictability of these environments, leading to suboptimal performance. This survey provides a comprehensive examination of how deep reinforcement learning (DRL) can be applied to solve these mathematical optimization problems in UAV communications and networking.Rather than simply introducing DRL methods, the focus is on demonstrating how these methods can be utilized to solve complex mathematical models of the underlying problems. We begin by reviewing the fundamental concepts of DRL, including value-based, policy-based, and actor-critic approaches. Then,we illustrate how DRL algorithms are applied to specific UAV network tasks by discussing from problem formulations to DRL implementation. By framing UAV communication challenges as optimization problems, this survey emphasizes the practical value of DRL in dynamic and uncertain environments. We also explore the strengths of DRL in handling large-scale network scenarios and the ability to continuously adapt to changes in the environment. In addition, future research directions are outlined, highlighting the potential for DRL to further enhance UAV communications and expand its applicability to more complex,multi-agent settings.

翻译：无人机在现代通信网络中扮演着日益关键的角色，为各类应用提供灵活性与增强的覆盖能力。然而，无人机网络因其动态与分布式的特性，尤其在处理功率分配、信道分配、缓存及任务卸载等任务时，带来了显著挑战。传统优化技术往往难以应对这些环境的复杂性与不可预测性，导致性能欠佳。本综述全面探讨了如何应用深度强化学习解决无人机通信与网络中的数学优化问题。重点并非简单介绍DRL方法，而在于展示如何利用这些方法解决底层问题的复杂数学模型。我们首先回顾DRL的基本概念，包括基于价值、基于策略及行动者-评论家方法。随后，通过从问题建模到DRL实现的讨论，阐述DRL算法在具体无人机网络任务中的应用。通过将无人机通信挑战构建为优化问题，本综述强调了DRL在动态不确定环境中的实用价值。我们还探讨了DRL在处理大规模网络场景中的优势及其持续适应环境变化的能力。此外，本文展望了未来研究方向，指出DRL在进一步提升无人机通信性能及扩展至更复杂多智能体应用场景方面的潜力。