公开(公告)号：US20220400062A1

公开(公告)日：2022-12-15

申请号：US17729318

申请日：2022-04-26

申请人： Guangdong Polytechnic Normal University

发明人： Jun CAI ,  Hongtian FU ,  Yan LIU ,  Jianzhen LUO ,  Liping LIAO

IPC分类号： H04L41/50 ,  H04L67/10 ,  G06F9/48 ,  G06N3/08

摘要： A method and apparatus for differentially optimizing a quality of service (QoS) includes: establishing a system model of a multi-task unloading framework; acquiring a mode for users executing a computation task, executing, according to the mode for users executing the computation task, the system model of the multi-task unloading framework; and optimizing a quality of service (QoS) on the basis of a multi-objective optimization method for a multi-agent deep reinforcement learning. According to the present invention, an unloading policy is calculated on the basis of a multi-user differentiated QoS of a multi-agent deep reinforcement learning, and with the differentiated QoS requirements among different users in a system being considered, a global unloading decision is performed according to a task performance requirement and a network resource state, and differentiated performance optimization is performed on different user requirements, thereby effectively improving a system resource utilization rate and a user service quality.