Level Generation

Level generation, the automated creation of game levels, aims to improve game development efficiency and create more diverse and engaging gameplay experiences. Current research focuses on enhancing controllability and validity of generated levels, employing techniques like conditional variational autoencoders, large language models (LLMs) fine-tuned for text-to-level generation, and reinforcement learning approaches, often incorporating quality-diversity algorithms. These advancements address challenges such as generating solvable and stylistically consistent levels, leading to more efficient game design pipelines and potentially revolutionizing how games are created and experienced.