Abstract: The invention provides a system of agents for use in an Urban Traffic Control environment, each agent representing a traffic light controller at a traffic junction to control traffic flow, said system comprising each agent is adapted to collect data local to the junction using one or more sensors and applying a Distributed W-Learning model to said collected data; each agent comprises means for mapping locally collected data to one of the system state representations available to determine action values; each agent is adapted to determine a current importance value of an action value using a Distributed W-Learning model. The main operational advantage of the system of the invention is that it utilizes machine learning to learn appropriate behaviours for a variety of traffic conditions, in a fully decentralized distributed self-organizing approach capable of addressing multiple performance policies on multiple agents, where data collection and analysis is performed by the junctions or intersections locally.