What’s more, the LLM is also able to extract a path from the level using the same system, as explained by the researchers in their paper. Prompts such as ‘many pipes, many enemies, little blocks’, or ‘no pipes, no enemies, many blocks’ will result in the LLM creating levels which reflect these characteristics, as it is still relying on GPT-2’s token prediction system. This method enables them to prompt certain characteristics using text. Using this mechanism, level blueprints are generated using a textual depiction, which can then be converted to a normal level using the tile key. GPT-2’s layers are then combined with a cross-attention mechanism, which passes information between an encoder and decoder for more reliable outputs. The model is then paired with a frozen text encoder based on BART, which functions as an input for the model. However, in the future, with bigger datasets and more complicated prompts, we may need to use a more sophisticated model like GPT3.”Ī visual representation of how the model ‘sees’ a level These converted levels are then fed into an LLM, powered by GPT-2, as training data.Īs to why the researchers picked GPT-2, Shyam Sudhakaran, the lead author on the paper, said, “I think with small datasets in general, GPT2 is better suited than GPT3, while also being much more lightweight and easier to train. For example, a ‘coin’ tile would be denoted by the character ‘o’. The model is first trained on a textual representation of Super Mario levels, which substitutes common ‘tiles’ or blocks for an ASCII symbol. Mario Gets A GPT Twistĭubbed as the world’s first ‘text-to-level’ model, MarioGPT is an algorithm that uses GPT-2 to create text-based level blueprints. According to them, this method not only performs better than techniques like procedural content generation, but can also provide ‘controllable level generation’ through text prompting. In a research paper published late last week, a group of researchers have detailed a method to use GPT-2 to generate tile-based game levels for old platforming games like Mario and Sonic. ![]() Researchers have found a way to generate Super Mario levels using large language models, opening the door for generative AI to influence game design in a big way. Games have always been used to train AI, but it seems the tables have turned now.
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