実世界で動作する強化学習ロボットを目指して-学習環境Re:ROSの試作-
2016 年 2016 巻 AGI-004 号 p. 06-
詳細
2016 年 2016 巻 AGI-004 号 p. 06-
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).
