What Is Reinforcement Learning?

The automatic learning of optimal decisions over time is a general and common problem that has been studied in many scientific and engineering fields. In our changing world, even problems that look like static input-output problems can become dynamic if time is taken into account. For example, imagine that you want to solve the simple supervised learning problem of pet image classification with two target classes—dog and cat. You gather the training dataset and implement the classifier using your favorite deep learning toolkit. After the training and validation, the model demonstrates excellent performance. Great! You deploy it and leave it running for a while. However, after a vacation at some seaside resort, you return to discover that dog grooming fashions have changed and a significant portion of your queries are now misclassified, so you need to update your training images and repeat the process again. Not so great!

This...