Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning
preprintposted on 27.07.2021, 10:39 by Zhenhui YeZhenhui Ye
In this paper, we aim to design a deep reinforcement learning(DRL) based control solution to navigate a swarm of unmanned aerial vehicles (UAVs) to fly around an unexplored target area under provide optimal communication coverage for the ground mobile users. Compared with existing DRL-based solutions that mainly solve the problem with global observation and centralized training, a practical and efficient Decentralized Training and Decentralized Execution(DTDE) framework is desirable to train and deploy each UAV in a distributed manner. To this end, we propose a novel DRL approach named Deep Recurrent Graph Network(DRGN) that makes use of Graph Attention Network-based Flying Ad-hoc Network(GAT-FANET) to achieve inter-UAV communications and Gated Recurrent Unit (GRU) to record historical information. We conducted extensive experiments to define an appropriate structure for GAT-FANET and examine the performance of DRGN. The simulation results show that the proposed model outperforms four state-of-the-art DRL-based approaches and four heuristic baselines, and demonstrate the scalability, transferability, robustness, and interpretability of DRGN.