用强化学习动态调控无人机通信网络，应对无人机数量变化。
Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew
- 基于强化学习设计自适应策略，应对无人机编队动态变化。
- 提出反应式与主动式两种策略，适用于普通及太阳能供电场景。
- 通过案例展示不同算法在动态无人机组中的应用效果。
基于无人机的通信网络（UCNs）是未来移动网络的关键组成部分。为应对UCN中动态环境带来的挑战，强化学习（RL）因其无需环境模型即可实现自适应决策的能力而成为有前景的解决方案。然而，现有大多数基于RL的研究集中在固定无人机数量下的控制策略设计，鲜有工作探讨当服务无人机数量动态变化时，如何自适应调节UCN。本文讨论了在动态无人机集合下基于强化学习的自适应UCN调控策略设计，涵盖一般UCN中的反应式策略和太阳能供电UCN中的主动式策略。首先概述了UCN架构与强化学习框架，随后详述潜在研究方向、关键挑战及可能解决方案。最后，通过部分近期研究成果作为案例，启发利用不同强化学习算法处理具有动态无人机编队的新型应用场景。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicle (UAV) based communication networks (UCNs) are a key component in future mobile networking. To handle the dynamic environments in UCNs, reinforcement learning (RL) has been a promising solution attributed to its strong capability of adaptive decision-making free of the environment models. However, most existing RL-based research focus on control strategy design assuming a fixed set of UAVs. Few works have investigated how UCNs should be adaptively regulated when the serving UAVs change dynamically. This article discusses RL-based strategy design for adaptive UCN regulation given a dynamic UAV set, addressing both reactive strategies in general UCNs and proactive strategies in solar-powered UCNs. An overview of the UCN and the RL framework is first provided. Potential research directions with key challenges and possible solutions are then elaborated. Some of our recent works are presented as case studies to inspire innovative ways to handle dynamic UAV crew with different RL algorithms.
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