Q-RAN: A Constructive Reinforcement Learning Approach for Robot Behavior Learning

Abstract— This paper presents a learning system that uses Qlearning with a resource allocating network (RAN) for behavior learning in mobile robotics. The RAN is used as a function approximator, and Q-learning is used to learn the control policy in ‘off-policy’ fashion that enables learning to be bootstrapped by a prior knowledge controller, thus speeding up the reinforcement learning. Our approach is verified on a PeopleBot robot executing a visual servoing based docking behavior in which the robot is required to reach a goal pose. Further experiments show that the RAN network can also be used for supervised learning prior to reinforcement learning in a layered architecture, thus further improving the performance of the docking behavior.

• Type: Conference
• Year: 2006
• Where: IROS
• Authors: Jun Li, Achim J. Lilienthal, Tomás Martínez-Marín, Tom Duckett
• Keywords: Behavior Learning, Docking Behavior, IROS 2006, Reinforcement Learning, Robotics