The invention discloses a multi-intersection signal cooperative control method based on deep reinforcement learning, and the method comprises the steps: firstly constructing a comprehensive reward function which comprises waiting time, queuing length and the influence of adjacent intersections, and constructing a mathematical model of a multi-intersection signal control system based on the reward function, so as to achieve the maximization of a comprehensive reward; and secondly, designing a deep Q network cooperative control algorithm for explicit strategy adjustment, and optimizing and adjusting adjustable strategy function parameters by introducing an explicit strategy adjustment function and utilizing a stochastic gradient descent method. And finally, dynamically adjusting the signal control strategy of each intersection in the road network in a complex and changeable traffic environment by designing a parameter sharing mechanism, thereby realizing collaborative optimization control of multiple intersections. According to the invention, the cooperative control efficiency of a multi-intersection signal system is obviously improved, traffic delay is reduced, and the traffic capacity of a road network is improved.
本发明公开了一种基于深度强化学习的多交叉口信号协同控制方法,首先,构建包含等待时间、排队长度及相邻交叉口影响的综合奖励函数,基于该奖励函数构建多交叉口信号控制系统的数学模型,以实现综合奖励最大化为目标。其次,设计显式策略调节的深度Q网络协同控制算法,通过引入显式策略调节函数,并利用随机梯度下降法对可调策略函数参数进行优化调整。最后,通过设计参数共享机制,在复杂多变的交通环境下,动态调整路网中各交叉口的信号控制策略,从而实现多交叉口的协同优化控制。本发明显著提高了多交叉口信号系统的协同控制效率,减少交通延误,提升路网通行能力。
Multi-intersection signal cooperative control method based on deep reinforcement learning
一种基于深度强化学习的多交叉口信号协同控制方法
2025-01-03
Patent
Electronic Resource
Chinese
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