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.

翻译：指令驱动游戏引擎（IDGE）项目旨在通过使大型语言模型（LLM）能够遵循自由形式的游戏规则并自主生成游戏过程，从而普及游戏开发。IDGE允许用户通过发出简单的自然语言指令来创建游戏，这显著降低了游戏开发的门槛。我们将IDGE的学习过程视为一个"下一状态预测"任务，其中模型在给定玩家动作的情况下自回归地预测游戏内状态。这是一项具有挑战性的任务，因为游戏内状态的计算必须精确；否则，微小的错误可能会破坏游戏进程。为了解决这个问题，我们以课程学习的方式训练IDGE，逐步增加模型对复杂场景的接触。我们的初步进展在于为扑克（一种广受喜爱的纸牌游戏）开发了一个IDGE。我们设计的引擎不仅支持多种扑克变体，还允许通过自然语言输入对规则进行高度定制。此外，它还支持基于极少样本快速原型化新游戏，提出了一种依赖最少提示工程和数据工程的游戏开发创新范式。这项工作为未来指令驱动游戏创作的进步奠定了基础，可能改变游戏的设计和游玩方式。