In recent years, we have witnessed an extremely rapid development of artificial intelligence in general use. Specifically, we are interested in the application of artificial intelligence for generation of art and assets. Although many criticize this direction, we believe it can be very useful in the future, especially in video game development. Artificial intelligence, particularly generative neural networks, is already used for generating textures and graphics. In our research, however, we focused on using generative adversarial neural networks to create levels or worlds in computer games that dynamically adapt to the user's actions. We used an open-source clone of the game Hill Climb Racing, designed for training virtually intelligent agents. We generated levels with our generative neural network, integrated them into the game, and then modified the levels with the neural network based on the agents' performance in solving them.
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