Deep Reinforcement Learning of an Autonomous Mobile Robot in Target Reaching Tasks Avoiding Obstacles

–Learning Performance of DDQN on Moving Obstacles–

Abstract

The authors have been studying on application of deep reinforcement learning methods to an autonomous mobile robot in target reaching tasks avoiding obstacles under uncertainties. In this report, randomly moving obstacles are newly introduced and effects of state selections on learning performance of Double DQN are examined. Learning simulations are performed using a robot model TurtleBot3 burger with an onboard 2D-LiDAR using a revised Gazebo simulator. As a result, it is confirmed that the number of success in target reaching avoiding obstacles during the learning process increases when the distance and the direction of the nearest obstacle from the robot are included in the states selection.