Tactical Communication Network Path Selection Algorithm Based on Deep Reinforcement Learning
Aiming at the problem that the heterogeneity of combat services,the diversity of link types,and the time-varying nature of link state make the decision-making space of end-to-end transmission paths that meet the service quality requirements rise exponentially,which makes it more difficult to match the service with the optimal path,a tactical communication network path optimization algorithm based on deep reinforcement learning(DRL-ST)is proposed.DRL-ST constructs an end-to-end transmission path decision model through Dueling DQN and uses the SumTree storage structure to optimize the sampling mechanism to improve the convergence speed of the model.Furthermore,on the basis of describing the end-to-end QoS parameters of the transmission path,a reward function based on multi-service characteris-tics is constructed to realize the optimal matching between the service quality requirements and the trans-mission path.The experimental results show that compared with the traditional algorithm,DRL-ST not only meets the service quality requirements but also reduces the end-to-end delay and packet loss rate by 16.78％and 28.43％,respectively,and the throughput is increased by 20.36％at most.
deep reinforcement learningsoftware defined networkpath optimizationquality of service
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