A Fast Estimation Network Model Based on Process Compression and an Optimized Parameter Search Algorithm for Q-Learning

The research content and main work of this paper are as follows: Analyzing the characteristics of the network traffic data set, the key index vacancy rate parameters in the process of cleaning the data set. This paper proposes a fast estimation network model based on process compression and an optimized parameter search algorithm for Q-Learning (QV-QL). The model starts from a predictive model based on deep learning, and on the basis of ensuring the functionality and certain accuracy of the model. Through the compression process and the introduction of mixed-precision calculations, the speed of searching for optimal parameters has been greatly improved.