This research investigates the application of different deep reinforcement learning methods for optimizing traffic light control in multi-modal urban traffic environments using the SUMO traffic simulator. Urban traffic congestion, with its significant economic, environmental, and social impacts, necessitates more sophisticated control strategies that can adapt to varying traffic conditions. Traditional traffic control systems, like fixed-time and adaptive methods, are often insufficient in handling the complexity of multi-modal traffic, which includes various traffic modes such as passenger cars and buses. Deep reinforcement learning, with its ability to dynamically optimize traffic light control without requiring prior knowledge of traffic patterns, is a promising method to improve traffic efficiency and achieve transit priority in multi-modal traffic. The research aims to address the limitations of the existing relevant research by employing several deep reinforcement learning algorithms, particularly multi-agent deep reinforcement learning methods, to coordinate multiple traffic lights in SUMO simulation. Research experiments are conducted in three different cases, which are set in road networks of different sizes respectively, and fixed traffic light control and max-pressure traffic light control are implemented for comparison. The applied deep reinforcement methods are evaluated in terms of training process and model evaluation. And the research results demonstrate that deep reinforcement learning methods, especially multi-agent deep reinforcement learning methods, can significantly enhance traffic flow efficiency and achieve transit priority in complex urban settings in multi modal simulation.