RL-Planner: Reinforcement Learning-Enabled Efficient Path Planning in Multi-UAV MEC Systems

Abstract

Mobile edge computing (MEC), located at the networks edge, enhances distributed computing. However, its fixed position presents limitations during emergencies. Integrating unmanned aerial vehicles (UAVs) into MEC systems offers a solution but introduces challenges in managing UAV collaboration. This paper proposes a Reinforcement Deep Q-Learning based multi-UAV MEC framework to optimize quality of service (QoS) and route planning. The proposed framework addresses these challenges by modeling user demand and using multi-factor optimization considering user demand, risk, and distance. A Markov Decision Process (MDP) models user demand for higher QoS. The reinforcement learning reward matrix incorporates terminal user demand, risk, and distance for efficient energy use and resource allocation. Simulations demonstrate the effectiveness of our proposed method, offering valuable insights for future research in this domain.