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Unmanned Aerial vehicles (UAVs) are widely used as network processors inmobile networks, but more recently, UAVs have been used in Mobile EdgeComputing as mobile servers. However, there are significant challenges to useUAVs in complex environments with obstacles and cooperation between UAVs. Weintroduce a new multi-UAV Mobile Edge Computing platform, which aims to providebetter Quality-of-Service and path planning based on reinforcement learning toaddress these issues. The contributions of our work include: 1) optimizing thequality of service for mobile edge computing and path planning in the samereinforcement learning framework; 2) using a sigmoid-like function to depictthe terminal users' demand to ensure a higher quality of service; 3) applyingsynthetic considerations of the terminal users' demand, risk and geometricdistance in reinforcement learning reward matrix to ensure the quality ofservice, risk avoidance, and the cost-savings. Simulations have shown theeffectiveness and feasibility of our platform, which can help advance relatedresearches.

Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning