The invention relates to a traffic signal control method based on multi-agent deep reinforcement learning. The method comprises the following steps: setting an intelligent agent for an intersection, deploying a fitting network at a cloud end or a server, and performing prediction according to a traffic road network structure and historical traffic flow data to obtain a traffic flow prediction result Y; training each intelligent agent and a fitting network by using the traffic flow prediction result and randomly acquired real-time traffic information of each intersection to obtain each optimized intelligent agent and an optimized fitting network; each optimized intelligent agent performs independent observation according to a traffic flow prediction result Y and respective current real-time traffic information, transmits the result to the optimized fitting network, calculates a global reward and feeds back the global reward to each intelligent agent, and each intelligent agent executes the next action according to the respective current observation result and reward. And forming a current optimal traffic signal control strategy of the target area. According to the invention, the traffic signal coordination control of multiple intersections is realized, and the travel efficiency is obviously improved.
本发明涉及基于多智能体深度强化学习的交通信号控制方法。该方法包括:为路口设置智能体,在云端或服务器部署拟合网络,根据交通路网结构和历史交通流数据进行预测并得到交通流预测结果Y;利用该交通流预测结果和随机获取的各个路口实时交通信息,对各个智能体和拟合网络进行训练,得到各个优化后的智能体和优化后的拟合网络;各个优化后的智能体根据交通流预测结果Y和各自当前的实时交通信息进行独立观测并将其结果传输给优化后的拟合网络,计算全局奖励并反馈给各个智能体,各个智能体根据当前各自观测结果和奖励,执行下一个动作,形成目标区域当前最优的交通信号控制策略。本发明实现了多路口的交通信号协调控制,明显提升出行效率。
Traffic signal control method based on multi-agent deep reinforcement learning
基于多智能体深度强化学习的交通信号控制方法
2025-01-03
Patent
Electronic Resource
Chinese
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