Comparison of Deep Reinforcement Learning Algorithms in a Robot Manipulator Control Application

Abstract

In this study, we apply deep reinforcement learning (DRL) to control a robot manipulator and investigate its effectiveness by comparing the performance of several DRL algorithms, namely, deep deterministic policy gradient (DDPG) and distributed distributional deterministic policy gradient (D4PG) algorithms. We conducted computational training and testing experiments on a control model for a reaching task of the robot manipulator. Experimental results show that the D4PG algorithm achieves a higher learning success rate than the DDPG algorithm and demonstrate the potential application of DRL for controlling robot manipulators.