Accuracy based fuzzy Q-learning for robot behaviours

This work presents a learning approach to fuzzy classifier systems. Q-learning algorithm is employed to implement credit assignment of the learning. GA operators are used as an action selection mechanism of the learning. The learning approaches can be viewed as a fuzzy learning classifier system or a Q-learning algorithm that adopts fuzzy logic to generalise Q-learning results. Rule accuracies are treated as rule fitness values. The learning algorithm is applied to a control robot behaviour.