Research Article
Improvement of Online Education Based on A3C Reinforcement Learning Edge Cache
@INPROCEEDINGS{10.1007/978-3-030-63941-9_18, author={Haichao Wang and Tingting Hou and Jianji Ren}, title={Improvement of Online Education Based on A3C Reinforcement Learning Edge Cache}, proceedings={6GN for Future Wireless Networks. Third EAI International Conference, 6GN 2020, Tianjin, China, August 15-16, 2020, Proceedings}, proceedings_a={6GN}, year={2021}, month={1}, keywords={Edge cache Reinforcement learning Online education}, doi={10.1007/978-3-030-63941-9_18} }- Haichao Wang
Tingting Hou
Jianji Ren
Year: 2021
Improvement of Online Education Based on A3C Reinforcement Learning Edge Cache
6GN
Springer
DOI: 10.1007/978-3-030-63941-9_18
Abstract
Online education is the complement and extension of campus education. Aiming at the situation that the mainstream network speed in online education scenarios is difficult to meet the requirements for smooth video playback, such as ultra-high-definition video resources, live broadcast, etc., this paper proposes an A3C-based online education resource caching mechanism. This mechanism uses A3C reinforcement learning-based edge caching to cache video content, which can meet the requirements of smooth video playback while reducing bandwidth consumption and improving network throughput. We use the Asynchronous Advantage Actor-Critic (A3C) technology of asynchronous advantage actors as a caching agent for network access. The agent can learn based on the content requested by the user and make a cache replacement decision. As the number of content requests increases, the hit rate of the cache agent gradually increases, and the training loss gradually decreases. The experimental comparison of LRU, LFU, and RND shows that this scheme can improve the cache hit rate.
