Enhanced multiagent multi-objective reinforcement learning for urban traffic light control

Traffic light control is one of the major problems in urban areas. This is due to the increasing number of vehicles and the high dynamics of the traffic network. Ordinary methods for traffic light control cause high rate of accidents, waste in time, and affect the environment negatively due to the high rates of fuel consumption. In this paper, we develop an enhanced version of our multiagent multi-objective traffic light control system that is based on a Reinforcement Learning (RL) approach. As a testbed framework for our traffic light controller, we use the open source Green Light District (GLD) vehicle traffic simulator. We analyze and fix some implementation problems in GLD that emerged when applying a more realistic continuous time acceleration model. We propose a new cooperation method between the neighboring traffic light agent controllers using specific learning and exploration rates. Our enhanced traffic light controller minimizes the trip time in major arteries and increases safety in residential areas. In addition, our traffic light controller satisfies green waves for platoons traveling in major arteries and considers as well the traffic environmental impact by keeping the vehicles speeds within the desirable thresholds for lowest fuel consumption. In order to evaluate the enhancements and new methods proposed in this paper, we have added new performance indices to GLD.