Multi-agent reinforcement learning for traffic congestion on one-way multi-lane highways

Abstract

In the last decade, agent-based modelling and simulation has emerged as a potential approach to study complex systems in the real world, such as traffic congestion. Complex systems could be modelled as a collection of autonomous agents, who observe the external environment, interact with each other and perform suitable actions. In addition, reinforcement learning, a branch of Machine Learning, that models the learning process of a single agent as a Markov decision process, has recently achieved remarkable results in several domains (e.g. Atari games, Dota 2, Go, Self-driving cars, Protein folding, etc.), especially with the invention of deep reinforcement learning. Multi-agent reinforcement learning, by taking advantage of these two approaches, is a new technique that can be used to further study complex systems. In this article, we present a multi-agent reinforcement learning model for traffic congestion on one-way multi-lane highways and experiment with six reinforcement learning algorithms in this setting.