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

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Computer Science and Education. Teaching and Curriculum (ICCSE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2024))

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Abstract

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.

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Acknowledgment

This work is funded by the National Natural Science Foundation of China under Grant No. 61772180, the Key R & D plan of Hubei Province (2020BHB004, 2020BAB012).

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Authors and Affiliations

  1. Wuhan FiberHome Information Integration Technologies Co., Ltd., Wuhan, China

    Shudong Zhang

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  1. Shudong Zhang
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Correspondence to Shudong Zhang .

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Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Wenxing Hong

  2. Xiamen University Malaysia, Sepang, Malaysia

    Geetha Kanaparan

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhang, S. (2024). A Fast Estimation Network Model Based on Process Compression and an Optimized Parameter Search Algorithm for Q-Learning. In: Hong, W., Kanaparan, G. (eds) Computer Science and Education. Teaching and Curriculum. ICCSE 2023. Communications in Computer and Information Science, vol 2024. Springer, Singapore. https://doi.org/10.1007/978-981-97-0791-1_2

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  • DOI: https://doi.org/10.1007/978-981-97-0791-1_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0790-4

  • Online ISBN: 978-981-97-0791-1

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