We propose a unified framework for coordinating Unmanned Aerial Vehicle (UAV) swarms operating under time-varying communication networks. Our framework builds on the concept of graphical games, which we argue provides a compelling paradigm to subsume the interaction structures found in networked UAV swarms thanks to the shared local neighborhood properties. We present a general-sum, factorizable payoff function for cooperative UAV swarms based on the aggregated local states and yield a Nash equilibrium for the stage games. Further, we propose a decomposition-based approach to solve stage-graphical games in a scalable and decentralized fashion by approximating virtual, mean neighborhoods. Finally, we discuss extending the proposed framework toward general-sum stochastic games by leveraging deep Q-learning and model-predictive control.

翻译：我们提议了一个统一框架来协调在时间变化式通信网络下运作的无人驾驶飞行器群(UAV),我们的框架以图形游戏的概念为基础,我们认为,这个框架为将网络化无人驾驶飞行器群(UAV)中的相互作用结构包含在内提供了令人信服的范式,这些互动结构来自共享的当地邻居地产。我们提出了一个基于集合的当地各州的合作无人驾驶飞行器群(UAV)的总和、可计算报酬功能,并为舞台游戏产生了纳什平衡。此外,我们提议了一种基于分解法的方法,通过接近虚拟的、中等的邻里以可缩放和分散的方式解决舞台-制图游戏。最后,我们讨论通过利用深度的学习和模型预测控制,将拟议框架扩大到一般和随机游戏。