Learning of Controlling Behavior Using Learning Automaton Networks with Communications Tatsushi Toyama, Non-member (Meidensha Corporation), Seiichi Koakutsu, Member (Chiba University), Fei Qian, Member (Hiroshima-DENKI Institute of Technology), Hironori Hirata, Member (Chiba University) The learning automaton is one of the best known learning systems which is capable of operating in highly uncertain random environments. In this paper, we propose a new learning system which accelerates the learning speed by introducing communications between learning automaton networks. In the proposed system, learning automaton networks can quickly respond to the dynamical changes of the random environment because they can share the information about the random environment by communications. We apply the proposed system to a learning problem of controlling the behavior of artificial insects. Computational experiments show that proposed system obtains better results in terms of the convergence speed comparing conventional learning automaton networks without communications.