Double Deep Q-Network algorithm for solving traffic congestion on one-way highways

Abstract

The problem of reducing traffic congestion on highways is one of the conundrums that the transport industry as well as the government would like to solve. With the great development of high technologies, especially in the fields of deep learning and reinforcement learning, the system using multi-agent deep reinforcement learning (MADRL) has become an effective method to solve this problem. MADRL is a method that combines reinforcement learning and multi-agent modeling and simulation approaches. In this article, we apply the Double Deep Q-Network (DDQN) algorithm to a multi-agent model of traffic congestion and compare it with two other algorithms.