This paper investigates how network structure influences the outcomes of reinforcement learning in a series of multi-agent simulations. Its basic results are the following: (i) contact between agents in networks creates similarity in the usage patterns of the signals these agents use; (ii) in case of complete networks, the bigger the network, the smaller the lexical differentiation; and (iii) in networks consisting of linked cliques, the distance between usage patterns reflects on average the structure of the network.