Collaborative Framework of Accelerating Reinforcement Learning Training with Supervised Learning Based on Edge Computing

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.