A Scheduling Method for Network Public Opinion Devices Based on Reinforcement Learning

Abstract

The present invention provides a method for scheduling network public opinion devices based on reinforcement learning, including: S1: establishing a deep reinforcement learning model; S2: scheduling a network public opinion device to perform target operation behaviors and recording the operation behaviors; S3: performing Before the scheduling of S2, judge the state of the Internet public opinion device according to the deep reinforcement learning model in S1, and then perform operations on the Internet public opinion device; S4: save the network when the execution of S3 is completed Record the state of the public opinion device and calculate the reward score based on it; S5: Calculate the loss value according to the reward score obtained in S4 to update the parameters of the deep reinforcement learning model; S6: According to the updated value in S5 The deep reinforcement learning model predicts network public opinion.

Description

technical field

[0001] The invention belongs to the field of automatic control, and in particular relates to a scheduling method of a network public opinion device based on reinforcement learning. Background technique

[0002] In recent years, the Internet has developed rapidly. As the fourth media after TV, radio, and newspapers, it has become an important carrier to reflect public opinion in society. On the other hand, due to the openness and virtuality of the Internet, online public opinion has become more and more complex, and its impact on real life is increasing day by day. Some major online public opinion events often have a greater influence on society. For government departments, public media and large enterprises, how to strengthen timely responses to Internet public opinion has become a major difficulty in Internet public opinion management. The network public opinion device cluster system is to build a network public opinion device with a distributed cluster str...

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