This research focuses on studying data-driven control with dynamics that are actively learned from machine learning algorithms. With system dynamics being identified using neural networks either explicitly or implicitly, we can apply control following either a model-based approach or a model-free approach. In this thesis, the two different methods are explained in detail and finally compared to shed light on the emerging data-driven control research field.\nIn the first part of the thesis, we first introduce state…