The Cooperative Driver: Multi-Agent Learning for Preventing Traffic Jams

The optimization of traffic flow on roads and highways of modern industrialized countries is key to their economic growth and success. Besides, the reduction of traffic congestions and jams is also desirable from an ecological point of view as it yields a contribution to climate protection. In this article, we stick to a microscopic traffic simulation model and interpret the task of traffic flow optimization as a multi-agent learning problem. In so doing, we attach simple, adaptive agents to each of the vehicles and make them learn, using a distributed variant of model-free reinforcement learning, a cooperative driving behavior that is jointly optimal and aims at the prevention of traffic jams. Our approach is evaluated in a series of simulation experiments that emphasize that the substitution of selfish human behavior in traffic by the learned driving policies of the agents can result in substantial improvements in the quality of traffic flow.