Procedural Content Generation (PCG) algorithms provide a technique to
generate complex and diverse environments in an automated way. However, while
generating content with PCG methods is often straightforward, generating
meaningful content that reflects specific intentions and constraints remains
challenging. Furthermore, many PCG algorithms lack the ability to generate
content in an open-ended manner. Recently, Large Language Models (LLMs) have
shown to be incredibly effective in many diverse domains. These trained LLMs
can be fine-tuned, re-using information and accelerating training for new
tasks. In this work, we introduce MarioGPT, a fine-tuned GPT2 model trained to
generate tile-based game levels, in our case Super Mario Bros levels. We show
that MarioGPT can not only generate diverse levels, but can be text-prompted
for controllable level generation, addressing one of the key challenges of
current PCG techniques. As far as we know, MarioGPT is the first text-to-level
model. We also combine MarioGPT with novelty search, enabling it to generate
diverse levels with varying play-style dynamics (i.e. player paths). This
combination allows for the open-ended generation of an increasingly diverse
range of content.

本文介绍一个 fine-tuned GPT2 模型 MarioGPT，它能够生成多样的游戏地图，实现可控制的 PCG。结合 Novelty search，该模型能够生成具有不同游戏动态特点的多样化地图，开创了文本 - 游戏地图的先河。