One of the very interesting properties of Reinforcement Learning algorithms is that they allow learning without prior knowledge of the environment. However, when the agents use algorithms that enable a generalization of the learning, they are unable to explain their choices. Neural networks are good examples of this problem. After a reminder about the basis of Reinforcement Learning, the Lattice Concept will be introduced. Then, Q-Concept-Learning, a Reinforcement Learning algorithm that enables a generalization of the learning, the use of structured languages as well as an explanation of the agent?s choices will be presented.