Traffic signal control (TSC) is an effective method for easing traffic congestion and enhancing traffic efficiency in urban areas, particularly with growing urbanization. Conventional traffic signal control techniques are unable to adapt swiftly to the intricate and dynamic road environment, necessitating a more intelligent approach to signal control. In recent years, there has been an increasing interest in employing reinforcement learning (RL) for TSC, which has displayed considerable potential in optimizing control strategies for complex traffic conditions. This work focuses on the application of RL in TSC for research purposes. To recreate real urban traffic conditions, a traffic simulation system was used in this study. Extensive experiments and comparative studies conducted in a simulated environment have illustrated the effectiveness and superiority of reinforcement learning methods in traffic signal control. This work primarily employs Deep Q-Network (DQN) in reinforcement learning to build the model and examines the variations in traffic signal timing under different reward mechanisms. The research findings demonstrate that reinforcement learning can significantly impact traffic signal control, effectively resolving current traffic signal control challenges and advancing the development of urban road network traffic efficiency.