Application of reinforcement learning with continuous state space to ramp metering in real-world conditions

Abstract

In this paper we introduce a new approach to Freeway Ramp Metering (RM) based on Reinforcement Learning (RL) with focus on real-life experiments in a case study in the City of Toronto. Typical RL methods consider discrete state representation that lead to slow convergence in complex problems. Continuous representation of state space has the potential to significantly improve the learning speed and therefore enables tackling large-scale complex problems. A robust approach based on local regression, named k nearest neighbors temporal difference (kNN-TD), is employed to represent state space continuously in the RL environment. The performance of the new algorithm is compared against the ALINEA controller and typical RL methods using a micro-simulation testbed in Paramics. The results show that RM using the kNN-TD method can reduce total network travel time by 44% compared to the do-nothing case (without RM) and by 17% compared to ALINEA.