In the last decade, we have seen many practical applications of Reinforcement Learning (RL) to different practical tasks, that obtained great success. The fields of applications are various, ranging from robotics and autonomous driving, to AI for video games and strategic games like chess. In particular, a key element for the success of these methods is without doubt the integration of RL with Deep Learning. Indeed, thanks to the advances in terms of computational power, today we have the possibility of training neural deep approximators to learn patterns from unstructured data like images or text. However, very recently, a new paradigm has emerged from traditional RL, called Offline Reinforcement Learning (Offline RL), in which every interaction between the agent and the environment is prohibited, so that the agent to be trained can only learn from previously collected datasets.