Transmission rate control in wireless LANs is one of the factors that affect communication quality. Many transmission rate control algorithms have been proposed in previous studies. However, there are cases where existing algorithms cannot adaptively control the rate due to the dynamics of wireless communication. In this paper, we propose a transmission rate control method based on Deep Q-Network (DQN), in which a DQN agent learns information about the communication environment and adaptively controls the transmission rate in response to the communication environment. We evaluate the proposed DQN-based transmission rate control by using the ns3-ai framework and the ns-3 network simulator. Simulations show that the proposed method improves throughput by up to 95\% compared to the Minstrel existing method.