Distributed traffic signal control method based on generative adversarial network and reinforcement learning

The invention discloses a method for accelerating a reinforcement learning (RL) algorithm by using an improved generative adversarial network (WGAN-GP) and applying the algorithm to regional traffic signal control, and the advantages of the generative adversarial network in the aspect of data generation and the advantages of the reinforcement learning algorithm in the aspect of learning a control strategy are applied to the regional traffic signal control. And the learning speed and effect of the signal control strategy can be effectively improved. The method mainly comprises: giving a control framework of multi-agent reinforcement learning in regional traffic signal control, and meanwhile, defining all elements of reinforcement learning, namely, states, actions, rewards and objective functions; defining a generative adversarial network structure; and proposing a data interaction framework of the generative adversarial network and reinforcement learning. 本发明公开了一种利用改进的生成对抗网络(WGAN-GP)加速强化学习(RL)算法并用于区域交通信号控制的方法，