We propose a novel model for mobile robot navigation using deep reinforcement learning. In our navigation tasks, no information about the environment is given to the robot beforehand. Additionally, the positions of obstacles and goal change in every episode. In order to succeed under these conditions, we combine several Q-learning techniques.