An Experimental Study on Reinforcement Learning on IoT Devices with Distilled Knowledge

Abstract

This paper provides an experimental study of reinforcement learning on IoT devices using distilled knowledge, whose a teacher with a well-trained model transfers to a student with a new model to be trained. The experimental results show that the distilled knowledge is effective to a new model training on IoT devices.