In this thesis, we contribute to new directions within Reinforcement Learning, which are important for many practical applications such as the control of biomechanical models. We deepen its mathematical foundations by deriving theoretical results inspired by classical optimal control theory and the connection between neural networks and differential equations. The resulting approach is applied to control complex motions.