Abstract
Artificial neural networks which have been used for agent learning have mostly employed back-propagation and recurrent neural networks. We, however, have observed that there exists another network structure in life—a small world network, which is used by C-elegance, a kind of eelworms. We examined not only the performance of the small world network but that of a regular graph network and a random graph network. We applied these three networks to agent learning problems, and when we compared them with back-propagation and recurrent neural networks, it became clear that in the case of small world network structures, it has the same or even better performance as compared to back-propagation and recurrent neural networks despite a lower number of synapses.
