基于深度强化学习的交通信号控制

The widely used traffic signal collaborative optimization model based on vehicle flow dynamics modeling has high accuracy but slightly weak transfer ability. To address this issue, this paper proposes a single agent traffic signal control method based on deep reinforcement learning. This method defines the action space for the first time considering pedestrian crossing interference at intersections, and defines three reward functions from three different perspectives, and proposes a cumulative delay approximation method. In terms of algorithm, a dynamic weight based soft actor-critic algorithm has been proposed, which can dynamically adjust the update amplitude of the actor network and critic network, significantly improving the convergence efficiency and performance of traditional soft actor-critic algorithm. The simulation results show that the proposed model and algorithm can effectively improve traffic performance indicators, such as reducing vehicle delay time, reducing vehicle parking times, and reducing vehicle queue length.