Summary
This concludes our foray into the world of reinforcement learning algorithms. We hope that this chapter provides adequate answers to those looking to understand the Q-learning algorithm, how to implement it in Scala, and how to apply it to leverage financial instruments. The chapter concludes with an overview of learning classifier systems.
This chapter completes our overview of some of the most frequently applied machine learning methods. It is important to acknowledge that the book does not pretend to cover all the types of machine learning algorithm such as k-nearest neighbors, decision trees, or random forests.
The ever-increasing amount of data that surrounds us requires data processing and machine learning algorithms to be highly scalable. This is the subject of the last part of the book: scalability.
