Summary

RL is one of the most exciting and fastest-growing branches of machine learning, with the greatest potential to create powerful optimization solutions to wide-ranging computing problems. As we have seen, Q-learning is one of the most accessible branches of RL and will provide a beginning RL practitioner and experienced programmer a strong foundation for developing solutions to both straightforward and complex optimization problems. 

In the next chapter, we'll learn about Q-learning in detail, as well as about the learning agent that we'll be training to solve our Q-learning task. We'll discuss how Q-learning solves MDPs using a state-action model and how to apply that to our programming task.