现有交通信号灯控制策略大多针对单一交叉口展开分析,该策略仅考虑车流量的单一因素,难以适应动态的路网状态.对此,提出了一种结合模糊控制的深度强化学习交通灯控制策略,利用SAC(soft actor critic)深度强化学习对两交叉口的交通信号灯相位选择及配时进行联合优化,同时考虑车辆速度、路段车辆排队长度等因素,利用模糊控制对SAC的惩罚函数进行处理.实验结果表明,与固定循环周期策略、SAC控制策略和DDPG(deep deterministic policy gradient)控制策略相比,提出的交通信号灯控制策略能获得更快的车辆通行速度,车辆的油耗和尾气排放情况也得到了改善.
Most of the existing traffic light control strategies consider a single factor such as traffic flow,which is difficult to adapt to the dynamic states of the road networks.In order to solve this problem,this paper proposed a deep reinforcement lear-ning traffic light control strategy combined with fuzzy control,used SAC deep reinforcement learning to jointly optimize the phase selection and timing of traffic lights at two intersections,while considering multiple influencing factors,used fuzzy control to process the penalty function of SAC.The experimental results demonstrate that compared with the fixed cycle strategy,SAC control strategy and DDPG control strategy,the proposed traffic signal control strategy can obtain faster vehicle speed,and the fuel consumption and exhaust emissions of the vehicle are also improved.