Toward Deep Reinforcement Learning of Robots in the Real World. -Prototyping of a Simulation Environment "Re:ROS"-

Abstract

Deep reinforcement learning has achieved great success in learning to play video games. In contrast to the video games in which the status changes discretely in space and time, robots in the real world move continuously and asynchronously following physical rules. To apply deep reinforcement learning to robot control, we prototyped a robot simulation environment "Re:ROS" with asynchronous system architecture based on Gazebo simulator and Robot Operating System (ROS).