混合动作空间下的多设备边缘计算卸载方法OA北大核心CSTPCD

In order to reduce the total cost of device-level in multi-device multi-edge server scenarios and solve the algo-rithm limitation of existing deep reinforcement learning(DRL)that only supports a single action space,a hybrid-based multi-agent deep determination policy gradient(H-MADDPG)is proposed.Firstly,the MEC system model is established by considering various complex environmental conditions,such as the dynamic change of computing power of IoT devices/servers with load,time-varying wireless transmission channel gain,unknown energy harvesting,and the uncertainty of task size.Then,the problem model is established with the minimum total cost of integrated delay and energy consumption in a continuous time slot as the optimization objective.Finally,the problem is delivered to H-MADDPG in the form of Markov decision process(MDP),which trains two parallel policy networks with the assistance of the value network,and outputs discrete server selection and continuous task offload rate.The experimental results show that the H-MADDPG method has good convergence and stability.From different perspectives,such as whether the computing tasks are inten-sive or delay sensitive,the overall system return of H-MADDPG is better than Local,OffLoad and DDPG.Compared with other methods,it can maintain greater system throughput under the demand of computationally intensive tasks.