  In this chapter, we will give comprehensive examples of applying RL in
optimizing the physical layer of wireless communications by defining different
class of problems and the possible solutions to handle them. In Section 9.2, we
present all the basic theory needed to address a RL problem, i.e. Markov
decision process (MDP), Partially observable Markov decision process (POMDP),
but also two very important and widely used algorithms for RL, i.e. the
Q-learning and SARSA algorithms. We also introduce the deep reinforcement
learning (DRL) paradigm and the section ends with an introduction to the
multi-armed bandits (MAB) framework. Section 9.3 focuses on some toy examples
to illustrate how the basic concepts of RL are employed in communication
systems. We present applications extracted from literature with simplified
system models using similar notation as in Section 9.2 of this Chapter. In
Section 9.3, we also focus on modeling RL problems, i.e. how action and state
spaces and rewards are chosen. The Chapter is concluded in Section 9.4 with a
prospective thought on RL trends and it ends with a review of a broader state
of the art in Section 9.5.
