This study combines reinforcement learning with finite element modeling to facial motion learning. A novel modeling workflow for learning facial motion was developed using a physicallybased model of the face within the Artisynth modeling platform, reinforcement learning algorithms were used to simulate facial movements. After the training, the agent improved symmetry by approximately 89% for symmetry-oriented motion and closely matched experimental data for smileoriented motion. This novel approach integrates finite element simulations into the reinforcement learning process, offering advanced rehabilitation programs for such patients.