Influence maximization (IM) has been widely studied in recent decades, aiming to maximize the spread of influence over networks. Despite many works for static networks, fewer research studies have been dedicated to the IM problem for dynamic networks, which creates many challenges. An IM method for such an environment, should consider its dynamics and perform well under different network structures. To fulfill this objective, more computations are required. Hence, an IM approach should be efficient enough to be applicable for the ever-changing structure of a network. In this research, an IM method for dynamic networks has been proposed which uses a deep Q-learning (DQL) approach. To learn dynamic features from the network and retain previously learned information, incremental and transfer learning methods have been applied. Experiments substantiate the good performance of the DQL methods and their superiority over compared methods on larger sizes of tested synthetic and real-world networks. These experiments illustrate better performance for incremental and transfer learning methods on real-world networks.