We encounter so many situations in the real world that are similar to that of the MABP we reviewed in this chapter. We could apply RL strategies to all these situations. The following are some of the real-world use cases similar to that of the MABP:

• Finding the best medicine/s among many alternatives
• Identifying the best product to launch among possible products
• Deciding the amount of traffic (users) that we need to allocate for each website
• Identifying the best marketing strategy for launching a product
• Identifying the best stocks portfolio to maximize profit
• Finding out the best stock to invest in
• Figuring out the shortest path in a given map
• Click-through rate prediction for ads and articles
• Predicting the most beneficial content to be cached at a router based upon the content of articles
• Allocation of funding for different departments of an organization
• Picking best-performing athletes out of a group of students given limited time and an arbitrary selection threshold

So far, we have covered almost all of the basic details that we need to know to progress to the practical implementation of RL to the MABP. Let's kick-start coding solutions to the MABP in our next section.