Architecture of behavior-based function approximator for adaptive control

Abstract

This paper proposes the use of behavior-based control architecture and investigates on some techniques inspired by Nature- a combination of reinforcement and supervised learning algorithms to accomplish the sub-goals of a mission of building adaptive controller. The approach iteratively improves its control strategies by exploiting only relevant parts of action and is able to learn completely in on-line mode. To illustrate this, it has been applied to non-linear, non-stationary control task: Cart-Pole balancing. The results demonstrate that our hybrid approach is adaptable and can significantly improve the performance of TD methods while speed up learning process. © 2009 Springer Berlin Heidelberg.