Neural reinforcement learning for behaviour synthesis

Abstract

We present the results of a research aimed at improving the Q-learning method through the use of artificial neural networks. Neural implementations are interesting due to their generalisation ability. Two implementations are proposed: one with a competitive multilayer perceptron and the other with a self-organising map. Results obtained on a task of learning an obstacle avoidance behaviour for the mobile miniature robot Khepera show that this last implementation is very effective, learning more than 40 times faster than the basic Q-learning implementation. These neural implementations are also compared with several Q-learning enhancements, like the Q-learning with Hamming distance, Q-learning with statistical clustering and Dyna-Q.