Inequity aversion reduces travel time in the traffic light control problem

Abstract

The traffic light control problem is to improve the traffic flow by coordinating between the traffic lights. Recently, a successful deep reinforcement learning model, CoLight, was developed to capture the influences of neighboring intersections by a graph attention network. We propose IACoLight that boosts up to 11.4% the performance of CoLight by incorporating the Inequity Aversion (IA) model that reshapes each agent's reward by adding or subtracting advantageous or disadvantageous reward inequities compared to other agents. Unlike in the other applications of IA, where both advantageous and disadvantageous inequities are punished by considering negative coefficients, we allowed them to be also rewarded and explored a range of both positive and negative coefficients. Our experiments demonstrated that making CoLight agents averse to inequities improved the vehicles' average travel time and rewarding rather than punishing advantageous inequities enhanced the results.