The invention particularly relates to a signal lamp intelligent control method based on deep reinforcement learning. According to the intelligent signal lamp control method based on deep reinforcement learning, a simulation platform is built based on SUMO simulation software and python, and a road network is built in the SUMO simulation software; designing a control strategy model of the on-ramp vehicle based on a flexible action-evaluation algorithm SAC; defining an algorithm process, and updating the target network to ensure the training stability; carrying out model training, and verifying the effectiveness of the model through a vehicle simulation technology; and finally, acquiring road vehicle information in real time, and realizing real-time control on the signal lamp by using the trained and verified model. The intelligent signal lamp control method based on deep reinforcement learning not only can control the phase of the signal lamp, but also can dynamically adjust the phase of the signal lamp according to the traffic flow density of each road, thereby balancing the waiting time of vehicles in each direction, and improving the passing efficiency of an intersection.
本发明特别涉及一种基于深度强化学习的信号灯智能控制方法。该基于深度强化学习的信号灯智能控制方法,基于SUMO仿真软件和python构建仿真平台,在SUMO仿真软件中搭建路网;基于柔性动作‑评价算法SAC设计入匝道车辆的控制策略模型;定义算法流程,对目标网络进行更新,以确保训练的稳定;进行模型训练,并通过车辆仿真技术验证模型的有效性;最后实时获取道路车辆信息,利用经过训练与验证的模型实现对信号灯的实时控制。该基于深度强化学习的信号灯智能控制方法,不仅能够控制信号灯相位,还可以根据各个道路的车流密度动态调整信号灯相位,进而平衡了各个方向车辆的等待时间,提高了路口的通行效率。
Signal lamp intelligent control method based on deep reinforcement learning
一种基于深度强化学习的信号灯智能控制方法
2024-12-06
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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