Summary

In this chapter, you were introduced to the basic concepts of reinforcement learning. After getting acquainted with the Gymnasium toolkit, you were presented with the MountainCar challenge, where a car needs to be controlled in a way that will allow it to climb the taller of two mountains. After solving this challenge using genetic algorithms, you were introduced to the next challenge, CartPole, where a cart is to be precisely controlled to keep an upright pole balanced. We were able to solve this challenge by combining the power of a neural network-based controller with genetic algorithm-guided training.

While we have primarily focused on problems involving structured numerical data thus far, the next chapter will shift its focus to applications of genetic algorithms in Natural Language Processing (NLP), a branch of machine learning that empowers computers to comprehend, interpret, and process human language.