Robot behavioral selection using Q-learning

Q-learning has often been used in robotics to learn primitive behaviors. However, the complexity of the algorithm increases exponentially with the number of states the robot can be in and the number of actions that it can take. Therefore, it is natural to try to reduce the number of states and actions in order to improve the efficiency of the algorithm. Robot behaviors and behavioral assemblages provide a good level of abstraction which can be used to speed up robot learning. Instead of coordinating a set of primitive actions, we use Q-learning to coordinate a set of well tested behavioral assemblages to accomplish a robotic target intercept mission.