If you have been following machine learning (ML) news, I am sure you will have encountered this kind of headline: computers performing better than world champions in various games. If you haven't, the following are sample news snippets from my quick Google search that are worth spending time reading to understand the situation:

• Check this out: https://www.theverge.com/2017/10/18/16495548/deepmind-ai-go-alphago-zero-self-taught/:

• See this: https://www.makeuseof.com/tag/ais-winning-5-times-computers-beat-humans/:

Reinforcement learning (RL) is a subarea of artificial intelligence (AI) that powers computer systems who are able to demonstrate better performance in games such as Atari Breakout and Go than human players.

In this chapter, we will look at the following topics:

• The concept of RL
• The multi-arm bandit problem
• Methods for solving the multi-arm bandit problem
• Real-world applications of RL
• Implementing a project using RL techniques to maximize our chances of winning at a multi-arm bandit machine