grandpere / mario-gpt

Generating Mario Levels with GPT2. Code for the paper "MarioGPT: Open-Ended Text2Level Generation through Large Language Models" https://arxiv.org/abs/2302.05981

MarioGPT: Open-Ended Text2Level Generation through Large Language Models

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How does it work?

MarioGPT is a finetuned GPT2 model (specifically, distilgpt2), that is trained on a subset Super Mario Bros and Super Mario Bros: The Lost Levels levels, provided by The Video Game Level Corpus. MarioGPT is able to generate levels, guided by a simple text prompt. This generation is not perfect, but we believe this is a great first step more controllable and diverse level / environment generation.

Requirements

• python3.8+

Installation

from pypi

pip install mario-gpt

or from source

git clone [email protected]:shyamsn97/mario-gpt.git
python setup.py install

Generating Levels

Since our models are built off of the amazing transformers library, we host our model in https://huggingface.co/shyamsn97/Mario-GPT2-700-context-length

This code snippet is the minimal code you need to generate a mario level!

from mario_gpt.lm import MarioLM
from mario_gpt.utils import view_level, convert_level_to_png

# pretrained_model = shyamsn97/Mario-GPT2-700-context-length

mario_lm = MarioLM()

prompts = ["many pipes, many enemies, some blocks, high elevation"]

# generate level of size 700
generated_level = mario_lm.sample(
    prompts=prompts,
    num_steps=699,
    temperature=2.0,
    use_tqdm=True
)

# show string list
view_level(generated_level, mario_lm.tokenizer)
...

See notebook for a more in depth tutorial to generate levels

Future Plans

Here's a list of some stuff that will be added to the codebase!

• Basic inference code
• Add MarioBert Model
• Inpainting functionality from paper
• Open-ended level generation code
• Training code from paper
• Different generation methods (eg. constrained beam search, etc.)

Authors

Shyam Sudhakaran [email protected], https://github.com/shyamsn97

Miguel González-Duque [email protected], https://github.com/miguelgondu

Claire Glanois [email protected], https://github.com/claireaoi

Matthias Freiberger [email protected], https://github.com/matfrei

Elias Najarro [email protected], https://github.com/enajx

Sebastian Risi [email protected], https://github.com/sebastianrisi

Citation

If you use the code for academic or commecial use, please cite the associated paper:

@misc{https://doi.org/10.48550/arxiv.2302.05981,
  doi = {10.48550/ARXIV.2302.05981},
  
  url = {https://arxiv.org/abs/2302.05981},
  
  author = {Sudhakaran, Shyam and González-Duque, Miguel and Glanois, Claire and Freiberger, Matthias and Najarro, Elias and Risi, Sebastian},
  
  keywords = {Artificial Intelligence (cs.AI), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {MarioGPT: Open-Ended Text2Level Generation through Large Language Models},
  
  publisher = {arXiv},
  
  year = {2023},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}