This paper presents the results of intelligent traffic light management (TLM) which improves the traffic efficiency via optimally controlling the green lights’ time interval and selecting the traffic phases. As the control problem is stochastic and difficult to be modeled accurately, model-free reinforcement learning (RL) is applied in this work. To stabilize the training process and mitigate the overestimation issue of conventional deep Q-learning based RL methods, we developed an RL algorithm with double deep Q-network (DQN) and a clipping function for the TLM problem with a discrete action space. The advantage of this clipped version of double DQN over other Q-learning-based algorithms is demonstrated in this work. Furthermore, the performance of RL-based TLM is compared with both fixed-time and adaptive rule-based TLM by using PTV Vissim which is a multi-modal traffic simulation software as the testing platform in this work.