Q-concept-learning: generalization with concept lattice representation in reinforcement learning

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.