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We propose a newmultiobjective control algorithm based on reinforcement learning for urban traffic signal control, namedmulti-RL. A multiagent structure is used to describe the traffic system. A vehicular ad hoc network is used for the data exchange among agents. A reinforcement learning algorithm is applied to predict the overall value of the optimization objective given vehicles' states. The policy which minimizes the cumulative value of the optimization objective is regarded as the optimal one. In order to make the method adaptive to various traffic conditions, we also introduce a multiobjective control scheme in which the optimization objective is selected adaptively to real-time traffic states. The optimization objectives include the vehicle stops, the average waiting time, and the maximum queue length of the next intersection. In addition, we also accommodate a priority control to the buses and the emergency vehicles through ourmodel. The simulation results indicated that our algorithm could performmore efficiently than traditional traffic light control methods.

Multiobjective Reinforcement Learning for Traffic Signal Control Using Vehicular Ad Hoc Network