Model Predictive Control (MPC) is widely used for trajectory optimization in robotics. This paper introduces a novel approach, using a generative adversarial network (GAN), to train Learnable-MPC policy when the demonstrator and imitator have different dynamics, achieving effective behavior learning without direct action copying.