The invention relates to a traffic light signal control method based on multi-agent reinforcement learning, and the method comprises the steps: obtaining real traffic data, and constructing and initializing a traffic environment; constructing a multi-agent reinforcement learning model for the traffic light of each intersection, wherein the multi-agent reinforcement learning model comprises an observation embedding layer, a self-adaptive neighbor cooperation layer and a Q value prediction layer; training a multi-agent reinforcement learning model; and sampling the observed value of the traffic environment of each intersection in real time at a pre-configured time interval, generating an optimal traffic light signal control scheme by using the trained multi-agent reinforcement learning model, and controlling the action of traffic lights. Compared with the prior art, the cooperative relation of the neighbor intersections is considered, the method can adapt to the complex road environment, and the optimal traffic light signal control scheme suitable for the intersection can be given for each intersection.
本发明涉及一种基于多智能体强化学习的交通灯信号控制方法,包括:获取真实交通数据,构建并初始化交通环境;针对每个交叉路口的交通灯构建多智能体强化学习模型,所述多智能体强化学习模型包括观测嵌入层、自适应邻居协作层和Q值预测层;训练多智能体强化学习模型;每隔预配置的时间间隔实时采样每个交叉路口的交通环境的观测值,利用训练完成的多智能体强化学习模型生成最优交通灯信号控制方案,并控制交通信号灯的行动。与现有技术相比,本发明考虑了邻居路口的协作关系,能够适应复杂的道路环境,并且能针对每个路口给出适合该交叉路口的最优交通灯信号控制方案。
Traffic light signal control method based on multi-agent reinforcement learning
一种基于多智能体强化学习的交通灯信号控制方法
2022-12-23
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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