Hierarchical learning of robot skills by reinforcement

Abstract

It is shown how reinforcement learning can be made practical for complex problems by introducing hierarchical learning. The agent at first learns elementary skills for solving elementary problems. To learn a new skill for solving a complex problem later on, the agent can ignore the low-level details and focus on the problem of coordinating the elementary skills it has developed. A physically-realistic mobile robot simulator is used to demonstrate the success and importance of hierarchical learning. For fast learning, artificial neural networks are used to generalize experiences, and a teaching technique is employed to save many learning trials of the simulated robot.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>