Development of a Reinforcement Learning System for Mobile Robots with ROS

Abstract

We propose a ROS (Robot Operating System) based reinforcement learning system for mobile robots. Previous ROS based reinforcement learning systems have a problem that a mobile robot state does not change by the mobile robot action. For example, if the mobile robot state is an image, learning the mobile robot action is difficult because the image does not change though the mobile robot action changes. To address the problem, the proposed system returns a reward and a next state when a mobile robot action, for example linear and angular velocity, is done by observing the wheel odometry of the mobile robot. We evaluate the proposed system in the simple visual navigation task that is implemented in ROS and Gazebo. Experiment results show that the proposed system works well and improves sample efficiency.