Articulatory synthesis is one of the approaches used for modeling human speech production. To improve the sample efficiency of the learning, researchers trained the model of speech production dynamics simultaneously with the policy. The policy was trained in a supervised way using predictions of the model of speech production dynamics. To stabilize the training, early stopping was incorporated into the algorithm. Additionally, researchers extracted acoustic features using an acoustic word embedding (AWE) model.