  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.
