Arterial traffic signal control can be modeled as a MAS and solved by the
reinforcement learning method. For a signalized arterial, the signal controller of each
intersection is an individually-motivated agent. The agents at different intersections
interact with each other and try to optimally control traffic along the arterial. Under the
framework of MAS, it is possible to decompose a complicated control system by
coordinating agents such that flexibility, efficiency, robustness, and cost effectiveness
can be achieved.