This paper describes the theoretical principles and experimental results of reinforcement learning algorithms embedded into IoT devices (Internet of Things), in order to tackle the problem of radio collision mitigation in ISM unlicensed bands. 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. how action and state spaces and rewards are chosen. In Section 9.3, we also focus on modeling RL problems, i.e. We present applications extracted from literature with simplified system models using similar notation as in Section 9.2 of this Chapter. Section 9.3 focuses on some toy examples to illustrate how the basic concepts of RL are employed in communication systems. We also introduce the deep reinforcement learning (DRL) paradigm and the section ends with an introduction to the multi-armed bandits (MAB) framework. Markov decision process (MDP), Partially observable Markov decision process (POMDP), but also two very important and widely used algorithms for RL, i.e. In Section 9.2, we present all the basic theory needed to address a RL problem, i.e.

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.

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