Dueling Double Deep Q-Network for Adaptive Traffic Signal Control with Low Exhaust Emissions in A Single Intersection

摘要

In order to reduce traffic exhaust emissions caused by the large quantities of vehicles,this paper studied the traffic signal control(TSC)model with low exhaust emissions on the basis of the deep reinforcement learning.In this study,the Dueling Double DQN with prioritized replay(DDDQN-PR)algorithm we proposed was combined with the Double DQN,Dueling DQN,and prioritized replay to achieve the goal of low exhaust emissions of TSC.The agent was trained in traffic simulator USTCMTS2.1 in a single intersection.The experimental results show that the performance of DDDQN-PR was significantly better than the other four algorithms,not only in data efficiency but also in final performance.