Control Method of Traffic Signal Lights Based On DDPG Reinforcement Learning

Abstract

Optimizing the traffic route in the current environment and how to control the best time for traffic lights in large-scale traffic flow has become the main factor in people’s current traffic travel life. This article proposes the use of deep deterministic policy gradients (DDPG). DDPG reinforces the method of learning traffic lights to control the best time for traffic lights, and studies the information interaction between the agent in the environment using the intersection as the agent, so that the agent can find the controllable target in the shortest time. The comparison of simulation experiments with traditional neural networks and reinforcement learning algorithms shows that the algorithm proposed in this paper is superior to other traditional algorithms in terms of solution time, and can quickly and effectively control the timing of traffic lights.