Instruction-Driven Game Engines on Large Language Models
<|reference_start|>Instruction-Driven Game Engines on Large Language Models: The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game rules and autonomously generate game-play processes. The IDGE allows users to create games by issuing simple natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a Next State Prediction task, wherein the model autoregressively predicts in-game states given player actions. It is a challenging task because the computation of in-game states must be precise; otherwise, slight errors could disrupt the game-play. To address this, we train the IDGE in a curriculum manner that progressively increases the model's exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, a universally cherished card game. The engine we've designed not only supports a wide range of poker variants but also allows for high customization of rules through natural language inputs. Furthermore, it also favors rapid prototyping of new games from minimal samples, proposing an innovative paradigm in game development that relies on minimal prompt and data engineering. This work lays the groundwork for future advancements in instruction-driven game creation, potentially transforming how games are designed and played.<|reference_end|>
@article{wu2024instruction-driven,
    title={Instruction-Driven Game Engines on Large Language Models},
    author={Hongqiu Wu, Yan Wang, Xingyuan Liu, Hai Zhao, Min Zhang},
    journal={arXiv preprint arXiv:2404.00276},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00276},
    primaryClass={cs.AI}
}