Design and implementation of a behavior-based control and learning architecture for mobile robots

Abstract

A behavior-based control and learning architecture is proposed, where reinforcement learning is applied to learn proper associations between stimulus and response by using two types of memory called as short Term Memory and Long Term Memory. In particular, to cope with delayed-reward problem, a knowledge-propagation (KP) method is proposed, where well-designed or well-trained S-R(stimulus-response) associations for low-level sensors are utilized to learn new S-R associations for high-level sensors, in case that those S-R associations require same objective such as obstacle avoidance. To show the validity of our proposed KP method, comparative experiments are performed for the cases that (i) only a delayed reward is used, (ii) some of S-R pairs are preprogrammed, (iii) immediate reward is possible, and (iv) our KP method is applied.