We provide a demonstration of offline Q-learning with parallel executing, in which we seperate the procedures of collecting data and training the model. First we collect data by interacting with the environment and save them to a replay memory file, and then fit and evaluate the Q network with the collected data. Repeat these two steps to improve the performance gradually.