Introduction

Learning and adapting to new circumstances is a crucial process for humans and, in general, for all animals. Usually, learning is intended as a process of trial and error through which we improve our performance in particular tasks. Our life is a continuous learning process, that is, we start from simple goals (for example, walking), and we end up pursuing difficult and complex tasks (for example, playing a sport). As humans, we are always driven by our reward mechanism, which awards good behaviors and punishes bad ones.

Reinforcement Learning (RL), inspired by the human learning process, is a subfield of machine learning and deals with learning from interaction. With the term "interaction," we mean the process of trial and error through which we, as humans, understand the consequences of our actions and build up our own experiences.

RL, in particular, considers sequential decision-making problems. These are problems in which an agent has to take a sequence...