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We propose a unified framework for coordinating Unmanned Aerial Vehicle (UAV)swarms operating under time-varying communication networks. Our frameworkbuilds on the concept of graphical games, which we argue provides a compellingparadigm to subsume the interaction structures found in networked UAV swarmsthanks to the shared local neighborhood properties. We present a general-sum,factorizable payoff function for cooperative UAV swarms based on the aggregatedlocal states and yield a Nash equilibrium for the stage games. Further, wepropose a decomposition-based approach to solve stage-graphical games in ascalable and decentralized fashion by approximating virtual, meanneighborhoods. Finally, we discuss extending the proposed framework towardgeneral-sum stochastic games by leveraging deep Q-learning and model-predictivecontrol.

Graphical Games for UAV Swarm Control Under Time-Varying Communication Networks