Acquiring Contour Following Behaviour in Robotics Through Q-Learning and Image-Based States
In this paper a visual and reactive contour following behaviour is learned by reinforcement. With artificial vision the environment is perceived in 3D, and it is possible to avoid obstacles that are invisible to other sensors that are more common in mobile robotics. Reinforcement learning reduces the need for intervention in behaviour design, and simplifies its adjustment to the environment, the robot and the task. In order to facilitate its generalisation to other behaviours and to reduce the role of the designer, the authors propose a regular image-based codification of states. Even though this is much more difficult, the implementation converges and is robust. Results are presented with a Pioneer 2 AT on a Gazebo 3D simulator.