Learning to Fly: A Distributed Deep Reinforcement Learning Framework for Software-Defined UAV Network Control
Control and performance optimization of wireless networks of Unmanned Aerial Vehicles (UAVs) require scalable approaches that go beyond architectures based on centralized network controllers.At the same time, the performance of model-based optimization approaches is often limited by the accuracy of the approximations and relaxations necessary olea