The invention provides a multi-agent reinforcement learning-based multi-traffic signal lamp control method and system, and relates to the technical field of signal lamp phase control. Road network structures of a plurality of intersections and traffic data of a preset time period are obtained, and agents of the intersections are constructed; constructing a state communication mechanism of multiple intersections by taking the traffic flow states of the exits of the upstream road sections of the adjacent intersections as communication contents; constructing a target reward function in combination with a continuous congestion penalty term; two networks with the same structure and different parameters are constructed for each intersection, and signal lamp coordination control of corresponding intelligent agents is executed for all intersections; and finally, network parameters are updated by adopting a loss function and a gradient descent method until the maximum simulation frequency is reached, and a signal lamp phase control strategy of multiple intersections is obtained. According to the invention, a state communication mechanism between intelligent agents is established, dynamic traffic flow characteristics between adjacent intersections are captured, and the traffic efficiency of a multi-intersection area can be effectively improved under real-time traffic data.
本发明提供的基于多智能体强化学习的多交通信号灯控制方法及系统,涉及信号灯相位控制技术领域,本发明通过获取多个交叉口的路网结构与预设时段的交通数据,构建各交叉口的智能体,将相邻交叉口的上游路段出口的车流状态作为通信内容,构建多交叉口的状态通信机制;然后结合连续拥堵惩罚项构建目标奖励函数;为每个交叉口构建两个结构相同、参数不同的网络,对所有交叉口执行对应智能体的信号灯协调控制;最后采用损失函数与梯度下降法更新网络参数,直到达到最大仿真次数,得到多交叉口的信号灯相位控制策略。本发明建立了智能体之间的状态通信机制,捕捉相邻交叉口之间的动态车流特征,在实时交通数据下,能有效提升多交叉口区域的通行效率。
Multi-agent reinforcement learning-based multi-traffic signal lamp control method and system
基于多智能体强化学习的多交通信号灯控制方法及系统
2024-11-15
Patent
Electronic Resource
Chinese
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