Traffic congestion remains a critical challenge for modern cities, causing economic loss, environmental damage, and reduced quality of life. Traditional signal control methods such as fixed-time and actuated systems cannot adapt to highly dynamic conditions, particularly in Hanoi, Vietnam, where irregular road layouts, non-lane-based driving, and motorcycle dominance create extreme variability. We propose GATLIGHT (Graph-Attention Traffic Light Control), a deep reinforcement learning framework that combines policy gradient optimization with a graph attention network to achieve decentralized yet coordinated signal control. Each intersection is modeled as an intelligent agent that exchanges attention-weighted information with its neighbors, enabling adaptive and network-aware decisions. The state representation encodes normalized vehicle counts and signal timing, while the reward minimizes the standard deviation of traffic distribution to balance flows and reduce bottlenecks. GATLIGHT is trained and evaluated in SUMO using both synthetic networks and real-world datasets from New York, Hangzhou, Jinan, and Hanoi. Compared to state-of-the-art methods such as PressLight and CoLight, GATLIGHT achieves up to 26.7% lower travel time and consistently reduces waiting time and queue length under real-world Hanoi traffic. These results demonstrate that graph-based reinforcement learning provides a scalable and robust solution for adaptive traffic management in some of the most complex and volatile urban environments. Code is available at: https://github.com/nvanhieu25/tsinghuaRL .