Dissipating stop-and-go waves in closed and open networks via deep reinforcement learning

Abstract

This article demonstrates the ability for model-free reinforcement learning (RL) techniques to generate traffic control strategies for connected and automated vehicles (CAVs) in various network geometries. This method is demonstrated to achieve near complete wave dissipation in a straight open road network with only 10% CAV penetration, while penetration rates as low as 2.5% are revealed to contribute greatly to reductions in the frequency and magnitude of formed waves. Moreover, a study of controllers generated in closed network scenarios exhibiting otherwise similar densities and perturbing behaviors confirms that closed network policies generalize to open network tasks, and presents the potential role of transfer learning in fine-tuning the parameters of these policies. Videos of the results are available at: https://sites.google.com/view/itsc-dissipating-waves.