Abstract
In this paper, efficiency improvement of reinforcement learning using parallel processing for combination value function. We propose the method of periodically composing Q table of local learning clusters to global Q table. We apply this method to two applications. One is maze problem and an another is behavior rule detection problem for modular typed robot. Q Learning method and Monte Carlo method are compared with profit share method that learns robot behaviors. We presented computer experiments of 40 PC clusters. The convergence time and learning times are evaluated and discussed.
