Multi-Intersection Traffic Signal Control Based on Multi-agent Reinforcement Learning: A Cooperative Approach

Adaptive traffic signal control has been widely investigated for several decades as an effective solution to mitigate urban traffic congestion. Recently, Multi-agent Reinforcement Learning (MARL) has emerged as a promising approach for optimizing traffic signal control in complex urban networks. However, the decision-making process becomes significantly more challenging in dynamic traffic environments involving multiple interconnected intersections. In such settings, effectively coordinating traffic signals using information collected from the Internet of Things remains a major challenge for improving the overall efficiency of road networks. Despite recent advances, existing MARL-based approaches still exhibit several limitations. First, many methods struggle to effectively integrate heterogeneous traffic information from complex traffic environments. Second, they often neglect the correlations among agents and fail to adequately capture the spatial–temporal dependencies between neighboring intersections. To address these challenges, this paper proposes a novel cooperative MARL-based approach for adaptive traffic signal control in multi-intersection networks. The proposed framework employs double deep Q-network architecture for each agent and introduces a cooperation mechanism that enables agents to share aggregated traffic information from neighboring intersections. Furthermore, a pheromone-based state representation is incorporated to model the collective traffic conditions and enhance coordination among agents. Extensive experiments conducted under different traffic scenarios demonstrate that the proposed approach significantly outperforms existing methods in relation to average pheromone intensity, average noise emission, and average waiting time. These results highlight the effectiveness of the proposed cooperative MARL framework in improving traffic efficiency and reducing congestion in multi-intersection urban traffic networks.