Throughout this book, we learned and implemented many reinforcement learning algorithms, but all this variety can be quite confusing when it comes to choosing one. For this reason, in this final chapter, we provided a rule of thumb that can be used to pick the class of RL algorithms that best fits your problem. It mainly considers the computational time and the sample efficiency of the algorithm. Furthermore, we provided some tips and tricks so that you can train and debug deep reinforcement learning algorithms better so as to make the process easier. 

We also discussed the hidden challenges of reinforcement learning: stability and reproducibility, efficiency, and generalization. These are the main issues that have to be overcome in order to employ RL agents in the physical world. In fact, we detailed unsupervised reinforcement learning and transfer learning, two strategies that can be used to greatly improve generalization and sample efficiency. 

Additionally, we detailed the most critical open problems and the cultural and technological impacts that reinforcement learning may have on our lives.

We hope that this book has provided you with a comprehensive understanding of reinforcement learning and piqued your interest in this fascinating field.