A deep reinforcement learning unloading algorithm,PPS-DDPG,was proposed for the task unloading problem in un-manned aerial vehicle-assisted mobile edge computing systems.The deep deterministic policy gradient(DDPG)algorithm,the improved priority experience replay mechanism,and annealing techniques were combined.A partial unloading strategy was adopted and the user scheduling,the resource allocation,and drone flight trajectories were jointly optimized under time-delay constraints,aiming to minimize the total energy consumption of end-users by establishing a mathematical model.The deep rein-forcement learning algorithm was used to find the optimal unloading decision.Through numerous simulation experiments,the algorithm is verified to be useful on effectively reducing terminal energy consumption and it outperforms the benchmark solution in terms of the performance and the convergence level.
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