  Unmanned Aerial vehicles (UAVs) are widely used as network processors in
mobile networks, but more recently, UAVs have been used in Mobile Edge
Computing as mobile servers. However, there are significant challenges to use
UAVs in complex environments with obstacles and cooperation between UAVs. We
introduce a new multi-UAV Mobile Edge Computing platform, which aims to provide
better Quality-of-Service and path planning based on reinforcement learning to
address these issues. The contributions of our work include: 1) optimizing the
quality of service for mobile edge computing and path planning in the same
reinforcement learning framework; 2) using a sigmoid-like function to depict
the terminal users' demand to ensure a higher quality of service; 3) applying
synthetic considerations of the terminal users' demand, risk and geometric
distance in reinforcement learning reward matrix to ensure the quality of
service, risk avoidance, and the cost-savings. Simulations have shown the
effectiveness and feasibility of our platform, which can help advance related
researches.
