Collaborative framework of accelerating reinforcement learning training with supervised learning based on edge computing

Yu Shan Lin, Chin Feng Lai, Chieh Lin Chuang, Xiaohu Ge, Han Chieh Chao

Research output: Contribution to journalArticlepeer-review

Abstract

In the reinforcement learning model training, it usually takes a lot of training data and computing time to find the law from the environmental response in order to facilitate the convergence of the model. However, edge nodes usually do not have powerful computing capabilities, which makes it impossible to apply reinforcement learning models to edge computing nodes. Therefore, the framework proposed in this study can enable the reinforcement learning model to gradually converge to the parameters of the supervised learning model within the shorter computing time, so as to solve the problem of insufficient terminal device performance in edge computing. Among the experimental results, the operating differences of hardware with different performance and the influence of the network environment and neural network architecture are analyzed based on the Mnist and Mall data sets. The result shows that it is sufficient to load the real-time required by users under the framework of collaborative training, and the time delay pressure on the model is caused by the application of different levels of complexity.

Original languageEnglish
Pages (from-to)229-238
Number of pages10
JournalJournal of Internet Technology
Volume22
Issue number2
DOIs
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

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