Fair Multi-Agent Reinforcement Learning for Traffic Control

In this paper, we address the issue of learning fair policies in decentralized cooperative multi-agent reinforcement learning (MARL), with a focus on traffic light control systems. We show that standard MARL algorithms that optimize the expected rewards often lead to unfair treatment across different intersections. To overcome this limitation, we design control policies that optimize a generalized Gini welfare function that explicitly encodes two aspects of fairness: efficiency and equity. Specifically, we propose three novel adaptations of MARL baselines that enable agents to learn decentralized fair policies, where each agent estimates its local value function while contributing to welfare optimization. We validate our approaches through extensive experiments across six traffic control environments with varying complexities and traffic layouts. The results demonstrate that our proposed methods consistently outperform existing MARL approaches both in terms of efficiency and equity.