Application of deep reinforcement learning tracking control of 3wd omnidirectional mobile robot

Abstract

Deep reinforcement learning, the fastest growing technique, to solve real-world complex problems by creating a simple mathematical framework. It includes an agent, action, environment, and a reward. An agent will in-teract with the environment, takes an optimal action aiming to maximize the total reward. This paper propos-es the compelling technique of deep deterministic policy gradient for solving the complex continuous action space of 3-wheeled omnidirectional mobile robots. Three-wheeled Omnidirectional mobile robots tracking is a difficult task because of the orientation of the wheels which makes it rotate around its own axis rather to follow the trajectory. A deep deterministic policy gradient (DDPG) algorithm has been designed to train in en-vironments with continuous action space to follow the trajectory by training the neural networks defined for the policy and value function to maximize the reward function defined for the tracking of the trajectory. DDPG agent environment is created in the Reinforcement learning toolbox in MATLAB 2019 while for Actor and crit-ic network design deep neural network designer is used. Results are shown to illustrate the effectiveness of the technique with a convergence of error approximately to zero.