Soft Robots are gaining popularity in the scientific community, due to the numerous applications for which they outperform their rigid counterparts. The trade-off stands in the difficulty to model and control them. In the last decade, the community has developed various advanced techniques to improve our ability to model them, with the purpose of developing effective controllers. This work presents a strategy to derive a model of the soft system and a controller for it: the model is obtained by learning the forward dynamics of the system purely from kinematic observational data and the controller comes from Model-Based Deep Reinforcement Learning using the learned forward dynamics.