Multi objective reinforcement learning driven task offloading algorithm for satellite edge computing networks

Satellite edge computing (SEC) has become a revolutionary paradigm to improve the quality of service, reduce the pressure on satellite-terrestrial backhaul bandwidth and reduce the average response delay of task requests. In this paper, we propose a task offloading algorithm based on K-D3QN to meet the rapidly growing demand of ground users. This algorithm improves the DQN algorithm by incorporating a satellite resource clustering module, a DDQN algorithm, and a competitive network mechanism module. The offloading decision-making process comprehensively considers three optimization objectives: task latency, resource utilization, and load-balancing degree, to achieve dynamic multi-objective optimization. Experimental results shown that the algorithm significantly reduces task latency, improves resource utilization and load-balancing degree.