Traffic signal control method based on asynchronous advantage actor-critic
A single intersection traffic signal control method based on the asynchronous advantage actor-critic(A3C)algorithm was proposed aiming at high cost of model learning and decision making in the existing traffic signal control methods based on deep reinforcement learning.Vehicle weight gain network was constructed from two different dimensions at the input side of the model,namely intersections and lanes,in order to preprocess the collected vehicle state information.A new reward mechanism was designed and an A3C algorithm that integrated vehicle weight gain networks was proposed.The simulation test results based on the microscopic traffic simulation software simulation of urban mobility(SUMO)show that the proposed method achieves better traffic signal control performance under three different traffic flow conditions of low,medium and high levels compared with traditional traffic signal control methods and benchmark reinforcement learning methods.
traffic signal controldeep reinforcement learningA3Cweight gain network
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