Towards Enhanced Fairness and Sample Efficiency in Traffic Signal Control

Traffic signal control (TSC) has seen substantial advancements through the application of reinforcement learning (RL) algorithms, which have shown remarkable potential in enhancing traffic flow efficiency. These RL-based approaches often surpass traditional rule-based methods, particularly in dynamic traffic environments. However, current RL solutions for TSC predominantly rely on model-free methods, necessitating extensive environmental interactions during training. This requirement can be prohibitively expensive or unfeasible in real-world implementations. Furthermore, existing methods have frequently neglected the issue of fairness in multi-intersection control, resulting in unbalanced congestion across different intersections. To address these challenges, we present FM2Light, a fairness-aware model-based multi-agent RL framework for TSC. Our approach leverages an ensemble of global world models for generating synthetic samples to enhance sample efficiency, thereby mitigating the data-intensive nature of the training process. Additionally, FM2Light incorporates a refined reward structure to promote fairness and improve coordination across multiple intersections. Extensive evaluations conducted in diverse real-world scenarios demonstrate that FM2Light achieves performance comparable to or exceeding that of model-free RL (MFRL) methods, while significantly reducing sample requirements and ensuring more equitable control among multiple agents.