Comparative Analysis of MADQN and QMIX Multi-Agent Reinforcement-Learning Methods for Urban Traffic Signal Control

In urban areas, where car congestion is increasing daily, improving traffic-signal control is a key area of study that directly affects people’s quality of life. It is expected that such improvement will reduce environmental traffic load and increase mobility. However, the inability to determine traffic load deterministically complicates the problem. Multi-agent reinforcement-learning approaches provide a solution thanks to their adaptive learning capabilities from instantaneous data. This study evaluates the Multi-Agent Deep Q-Network (MADQN) algorithm and the QMIX value decomposition method (QMIX) in an urban traffic network with 16 signalized intersections, comparing them with Fixed-Time and Max-Pressure. Experiments were conducted under three vehicle-density levels within the same network geometry and phase-matching rules. Performance was evaluated using waiting time, travel time, speed, efficiency, carbon dioxide (CO2) emissions, time to collision below 1 s (TTC < 1 s), and post-encroachment time below 1 s (PET < 1 s).. In all demand scenarios, both reinforcement-learning controllers achieved successful results. MADQN consistently provided lower average waiting times, whereas QMIX consistently achieved higher efficiency and, in some settings, lower CO2 and lower cross-seed variation. No statistical superiority between MADQN and QMIX was established. Overall, the results support the value of adaptive control in this test environment and indicate trade-offs among efficiency, emissions, and proxy safety.