HeteroLight: A General and Efficient Learning Approach for Heterogeneous Traffic Signal Control

Efficient and scalable adaptive traffic signal control is crucial in reducing congestion, maximizing through-put, and improving mobility experience in ever-expanding cities. Recent advances in multi-agent reinforcement learning (MARL) with parameter sharing have significantly improved the adaptive optimization of large-scale, complex, and dynamic traffic flows. However, the limited model representation capability due to shared parameters impedes the learning of diverse control strategies for intersections with different flows/topologies, posing significant challenges to achieving effective signal control in complex and varied real-world traffic scenarios. To address these challenges, we present a novel MARL-based general traffic signal control framework, called HeteroLight. Specifically, we first introduce a General Feature Extraction (GFE) module, crafted in a decoder-only fashion, where we employ an attention mechanism to facilitate efficient and flexible extraction of traffic dynamics at intersections with varied topologies. Additionally, we incorporate an Intersection Specifics Extraction (ISE) module, designed to identify key latent vectors that represent the unique intersection’s topology and traffic dynamics through variational inference techniques. By integrating the learned intersection-specific information into policy learning, we enhance the parameter-sharing mechanism, improving the model’s representation diversity among different agents and enabling the learning of a more efficient shared control strategy. Through comprehensive evaluations against other state-of-the-art traffic signal control methods on the real-world Monaco traffic network, our empirical findings reveal that HeteroLight consistently outperforms other methods across various evaluation metrics, highlighting its superiority in optimizing traffic flows in heterogeneous traffic networks.