MarioGPT: Open-Ended Text2Level Generation through Large Language Models

S Sudhakaran, M González-Duque, C Glanois, M Freiberger, E Najarro, S Risi
[IT University of Copenhagen]

MarioGPT: 基于大型语言模型的开放式Text2Level生成

要点:

  1. MarioGPT 是一个微调的GPT-2模型，通过自然语言提示生成超级马里奥兄弟的关卡；

  2. 该模型可预测玩家的互动，生成多样化和可玩的环境，并减少对昂贵的外部主体互动的需求；

  3. 将 MarioGPT 与新颖性搜索等多样性驱动算法相结合，可以实现开放式和功能性的内容生成；

  4. 将 GPT-n 这样的大型语言模型用于 PCG 方法，为未来研究开辟了新的方向。

一句话总结:
MarioGPT 是一个经过微调的 GPT-2 模型，可以根据自然语言提示生成超级马里奥兄弟的关卡，并且可以与新奇性搜索相结合，以开放的方式产生多样化的关卡。

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.

https://arxiv.org/abs/2302.05981


图片
图片
图片
图片

内容中包含的图片若涉及版权问题，请及时与我们联系删除