Play Video Games with Generative AI: GAIL
In the preceding chapters, we have seen how we can use generative AI to produce both simple (restricted Boltzmann machines) and sophisticated (variational autoencoders, generative adversarial models) images, musical notes (MuseGAN), and novel text (BERT, GPT-3).
In all these prior examples, we have focused on generating complex data using deep neural networks. However, neural networks can also be used to learn rules for how an entity (such as a video game character or a vehicle) should respond to an environment to optimize a reward; as we will describe in this chapter, this field is known as reinforcement learning (RL). While RL is not intrinsically tied to either deep learning or generative AI, the union of these fields has created a powerful set of techniques for optimizing complex behavioral functions.
In this chapter, we will show you how to apply GANs to learn optimal policies for different figures to navigate within...
