Towards Enhanced Fairness and Sample Efficiency in Traffic Signal Control

Traffic signal control (TSC) has seen substantial advancements through the application of reinforcement learn- ing (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, necessitat- ing 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 in- tersections. 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.