Combining Computer Vision and Reinforcement Learning

In the previous chapter, we learned how to generate images of interest. In this chapter, we will learn how to combine reinforcement learning-based techniques (primarily, deep Q-learning) with computer vision-based techniques. This is especially useful in scenarios where the learning environment is complex and we cannot gather data for all the cases. In such scenarios, we want the model to learn by itself in a simulated environment that resembles reality as closely as possible. Such models come in handy when used for self-driving cars, robotics, bots in games (real as well as digital), and the field of self-supervised learning, in general.

We will start by learning about the basics of reinforcement learning, and then about the terminology associated with identifying how to calculate the value (Q-value) associated with taking an action in a given state. Then, we will learn about filling a Q-table, which helps to identify the value...