Title: English information teaching resource sharing based on deep reinforcement learning

Authors: Chao Han

Addresses: Department of Foreign Language, Kaifeng University, Kaifeng 475004, China

Abstract: In order to solve the problems of low energy efficiency, poor stability and low convergence in the existing teaching resource sharing methods, an English information teaching resource sharing method based on deep reinforcement learning is proposed. Firstly, a deep reinforcement learning model under super dense network is constructed. Secondly, with the support of the in-depth reinforcement model, this paper optimises the sharing interference and sharing efficiency of English information teaching resources. Finally, combined with the resource sharing model of deep reinforcement learning, the resource sample training is carried out to realise the sharing of teaching resources. The experimental results show that when the number of small base stations is 12, the average shared energy efficiency of the English information-based teaching resource sharing method based on deep reinforcement learning is about 1 bit/J, and the average energy efficiency fluctuation is ±0.05, indicating that the proposed method has good stability and adaptability.

Keywords: deep reinforcement learning; English information teaching resources; resource sharing.

DOI: 10.1504/IJCEELL.2024.135269

International Journal of Continuing Engineering Education and Life-Long Learning, 2024 Vol.34 No.1, pp.53 - 65

Received: 25 Oct 2021
Accepted: 12 Jan 2022
Published online: 03 Dec 2023 *

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