This paper is particularly concerned with the representation of an agent behaviour suitable to model autonomous and efficient traffic control at urban network junctions. Network control efficiency is expected thus to emerge from these simpler autonomous entities cohabitating in a common environment. A fuzzy Q-learning approach is used to implement the agent-based reasoning and decision-making mechanisms and some preliminary simulation experiments are set up so as to assess the feasibility of our model to cope with urban traffic coordination.