Reinforcement learning

Reinforcement learning is an artificial intelligence approach that focuses on the learning of the system through its interactions with the environment. In reinforcement learning, the system's parameters are adapted based on the feedback obtained from the environment, which in turn provides feedback on the decisions made by the system. The following diagram shows a person making decisions in order to arrive at their destination. Let's take an example of the route you take from home to work:

In this case, you take the same route to work every day. However, out of the blue, one day you get curious and decide to try a different route with a view to finding the shortest path. Similarly, based on your experience and the time taken with the different route, you'd decide whether you should take a specific route more often. We can take a look at one more example in terms of a system modeling a chess player. In order to improve its performance, the system utilizes the result of its previous moves; such a system is said to be a system learning with reinforcement.

So far, we have learned the basic working principles of ML and different learning tasks. However, a summarized view of each learning task with some example use cases is a mandate, which we will see in the next subsection.