École Nationale Supérieure de
Techniques Avancées

Abstract

Reinforcement learning is a state-of-art policy learning model, which is widely used in the aspect of robots and automation. During my three-month internship, I have worked with my team on the continual learning of reinforcement learning models. My job is to reproduce the experiment results on the real robot. Using ROS, we have managed to manipulate a three-wheel robot, Omnirobot, to do its tasks. We have used dynamic randomisation when training the model in a simulation environment. Then we applied the model on the real robot, and found that the result is not similar to that of simulation. We will also discuss about the factors of the discrepancy in the paper.