In this chapter, we learned about RL. We started the chapter by defining RL and its difference when compared with other ML techniques. We then reviewed the details of the MABP and looked at the various strategies that can be used to solve this problem. Use cases that are similar to the MABP were discussed. Finally, a project was implemented with UCB and Thompson sampling algorithms to solve the MABP using three different simulated datasets.

We have almost reached the end of this book. The appendix of this book, The Road Ahead, as the name reflects, is a guidance chapter suggesting details on what's next from here to become a better R data scientist. I am super excited that I am at the last leg of this R projects journey. Are you with me on this as well?