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About this paper
Huan Chang, Yicheng Chen, Baochang Zhang, David Doermann
This paper develops a reinforcement learning-based platform for multi-UAV mobile edge computing and path planning, which integrates QoS optimization, collision avoidance, and risk management within a unified framework. It demonstrates improved Quality of Service and robust navigation in dynamic environments compared to traditional methods by incorporating a novel sigmoid-like function for user demand and a comprehensive, tunable reward structure.
Key insights
- A unified reinforcement learning framework can effectively integrate multi-UAV path planning with mobile edge computing (MEC) resource allocation, considering both quality of service (QoS) and risk avoidance.
- Modeling terminal user demand with a sigmoid-like function, rather than a linear one, leads to higher QoS and faster service completion by better prioritizing users with greater needs.
- A comprehensive, tunable reward function allows the platform to balance conflicting objectives, such as maximizing QoS, minimizing risk (including collision avoidance with other UAVs), and optimizing path efficiency.
Summary by alphaXiv.
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