MarioGPT：通过大型语言模型实现开放式文本生成关卡
MarioGPT: Open-Ended Text2Level Generation through Large Language Models
AI总结：
本文提出微调GPT2模型MarioGPT，利用文本提示生成《超级马里奥兄弟》关卡，并结合新颖性搜索实现可控、多样且开放式的关卡演化。
摘要：
程序化内容生成（PCG）是一种以自动化方式生成复杂且多样环境的技术。然而，尽管使用PCG方法生成内容通常很直接，但生成能够反映特定意图和约束的有意义内容仍然具有挑战性。此外，许多PCG算法缺乏以开放式方式生成内容的能力。近来，大型语言模型（LLM）已在许多不同领域表现出极其显著的效果。这些经过训练的LLM可以进行微调，从而复用信息并加快新任务的训练。本文提出MarioGPT，这是一个经过微调的GPT2模型，经过训练可生成基于图块的游戏关卡，在我们的研究中即《超级马里奥兄弟》关卡。MarioGPT不仅能够生成多样的关卡，还可以通过文本提示实现可控的关卡生成，从而应对当前PCG技术的关键挑战之一。据我们所知，MarioGPT是首个文本到关卡模型；结合新颖性搜索，它能够生成具有不同玩法动态（即玩家路径）的多样关卡，并开放式地发现范围日益多样的内容。代码可在https://github.com/shyamsn97/mario-gpt获取。
英文摘要：
Procedural Content Generation (PCG) is 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. Here, we introduce MarioGPT, a fine-tuned GPT2 model trained to generate tile-based game levels, in our case Super Mario Bros levels. 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 and combined with novelty search it enables the generation of diverse levels with varying play-style dynamics (i.e. player paths) and the open-ended discovery of an increasingly diverse range of content. Code available at https://github.com/shyamsn97/mario-gpt.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
