Collaborative Framework of Accelerating Reinforcement Learning Training with Supervised Learning Based on Edge Computing,ERICDATA高等教育知識庫
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篇名
Collaborative Framework of Accelerating Reinforcement Learning Training with Supervised Learning Based on Edge Computing
並列篇名
Collaborative Framework of Accelerating Reinforcement Learning Training with Supervised Learning Based on Edge Computing
作者 Yu-Shan LinChin-Feng LaiChieh-Lin ChuangXiaohu GeHan-Chieh Chao
英文摘要
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.
起訖頁 229-238
關鍵詞 Edge computingReinforcement learningCollaborative frameworkSupervised learning
刊名 網際網路技術學刊  
期數 202103 (22:2期)
出版單位 台灣學術網路管理委員會
DOI 10.3966/160792642021032202001  複製DOI
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