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Multi-UAV Navigation for Partially Observable Communication Coverage by Graph Reinforcement Learning
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  • Zhenhui Ye ,
  • Ke Wang ,
  • Yining Chen ,
  • Xiaohong Jiang ,
  • Guanghua Song
Zhenhui Ye
Zhejiang University, Zhejiang University

Corresponding Author:[email protected]

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Yining Chen
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Xiaohong Jiang
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Guanghua Song
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Abstract

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.
01 Jul 2023Published in IEEE Transactions on Mobile Computing volume 22 issue 7 on pages 4056-4069. 10.1109/TMC.2022.3146881