Biped Robot Walking based on Deep Reinforcement Learning

Abstract

In this paper a 6 DOF biped robot model is designed, and deep reinforcement machine learning methods are implemented for the robot to learn efficient walking following a straight line. Detailed procedure of the robot design, development of a Simulink model and implementation of learning procedures is presented. Two approaches were compared for motion learning – Deep Deterministic Policy Gradient (DDPG), and Twin-Delayed Deep Deterministic Policy Gradient (TD3). The results show that both approaches are successful in generating a model with free continuous action learning and input to action mapping. Additionally, our results show that the TD3 algorithm outperforms the DDPG algorithm in the problem as formulated in this study.