Proposed in this paper is a multi-interaction history learning approach for the coordination of urban intersection agents.In the investigation,first,each signalized intersection is defined with an Agent controller.Next,a multi-interaction model for urban intersection Agents is built based on the two-person Nash equilibrium game theory to make each intersection Agent to perform multi-interaction learning with its neighbours and to update its mixed strategy according to the utility value of the selected strategy.Then,the iterative interaction learning process of intersection Agents is analyzed by using the parameters such as memory factor δ,learning probability α and local traffic change probability βi at each intersection.A multi-interactive history learning algorithm was constructed.In the proposed algorithm,intersection Agents coordinate by taking into consi-deration all history interactive information（especially the recent one） coming from neighbouring intersection Agents.Finally the effects of parameters δ,α and βi on the algorithm performance is also analyzed by the experiment of traffic signal control at some connected intersections.The results show that the proposed coordinative learning approach is effective.