The invention provides a traffic signal control method based on multi-agent reinforcement learning and intersection importance, and belongs to the technical field of traffic signal control. Constructing a CM-MG model based on the priority of the intersection, and setting a decision sequence for all agents in the traffic grid; and selecting an optimal action for each agent in the decision sequence through the GNSD-Light network, wherein the method comprises the following steps of: obtaining environment information through an observation presentation layer; historical action characteristics of each agent are extracted through a precursor action presentation layer, so that member agents understand and cooperate with a precursor decision; outputting the optimal action of each agent in sequence through a Q output layer; the observation presentation layer comprises the following steps: extracting spatial features through a simple spatial processing module based on relative position coding; and integrating neighborhood observation features through a residual image attention network. The traffic efficiency of the important intersection is improved; and thus, the traffic efficiency of global traffic is improved.
本发明提供基于多智能体强化学习和路口重要性的交通信号控制方法,属于交通信号控制技术领域。基于交叉口优先级构建CM‑MG模型,为交通网格中所有智能体设置决策序列;通过GNSD‑Light网络为决策序列中每个智能体选择最佳动作,包括:通过观测表示层获取环境信息;通过前驱动作表示层提取每个智能体的历史动作特征,使成员智能体理解并协作前驱的决策;通过Q输出层顺序输出每个智能体的最佳动作;观测表示层,包括:通过基于相对位置编码的简洁空间处理模块,提取空间特征;通过残差图注意力网络整合邻域观测特征。本发明提高了重要交叉口的通行效率;进而提高了全局交通的通行效率。
Traffic signal control method based on multi-agent reinforcement learning and intersection importance
基于多智能体强化学习和路口重要性的交通信号控制方法
2025-02-14
Patent
Electronic Resource
Chinese
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