A modifled approach to fuzzy Q learning for mobile robots

Abstract

A modified approach to fuzzy Q-learning is presented in this paper. A reward sharing mechanism is added to increase the learning speed and to allow treatment of each fuzzy rule as a separate learning node. A new method of exploration is also proposed to increase the learning performance. Two basic robot behaviours which are a goal-seeking and an obstacle avoidance behaviour are simulated to show the promise of the proposed techniques. The goal-seeking behaviour is implemented on a real robot. The experimental results show that this method is practical for a real-world problem.