Reinforcement learning is an automatic learning technique that aims to implement systems able to learn and adapt to the changes in the environment in which they are immersed, through the distribution of a reward called reinforcement, which consists of evaluating their performance. It can be implemented by means of different algorithms, such as Q-learning, to be inserted into the system in which learning is to be carried out. This technology is increasingly widespread, thanks to its ability to interact with the environment.
In this chapter, we will summarize what has been covered so far in this book, and what the next steps are from this point on. You will learn how to apply the skills you have gained to other projects and real-life challenges in building and deploying reinforcement learning models, and other common technologies that data scientists often use...