In the previous chapter, we learned about how to combine NLP techniques (LSTM and transformer) with computer vision-based techniques. In this chapter, we will learn how to combine reinforcement learning-based techniques (primarily deep Q-learning) with computer vision-based techniques.
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. Next, we will learn about filling a Q-table, which helps in identifying the value associated with various actions in a given state. Furthermore, we will learn about identifying the Q-values of various actions in scenarios where coming up with a Q-table is infeasible due to a high number of possible states; we'll do this using...
