Q-learning has often been used in robotics to learn primitive behaviors. However, the complen'ty of the algorithm increases ezponentially with the number of stat= the mbot can be an and the number of actiom that it con take. Therefore, it is natuml to try to Educe the number of states and actions in order to impmwe the eficiency of the algorithm. Robot behauiors 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 behavioml assemblages to accomplish a robotic target intercept mission.
['Eric Martinson', 'Alexander Stoytchev', 'Ronald C. Arkin']
Robot behavioral selection using q-learning