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）遵循自由格式的游戏规则并自主生成游戏进程，来实现游戏开发的民主化。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的学习过程建模为下一状态预测任务，其中模型根据玩家的行动自回归地预测游戏内状态。由于游戏内状态的计算必须精确，否则微小误差可能破坏游戏进程，因此这是一项具有挑战性的任务。为解决此问题，我们采用渐进式课程学习方式训练IDGE，逐步增加模型对复杂场景的暴露程度。我们最初取得的进展是为扑克这一广受欢迎的纸牌游戏开发IDGE。我们设计的引擎不仅支持多种扑克变体，还能通过自然语言输入高度自定义规则。此外，该引擎还支持基于最小样本快速原型化新游戏，提出了一种依赖极简提示和数据工程的游戏开发新范式。这项工作为未来指令驱动游戏创作的进展奠定了基础，有望变革游戏的设计与游玩方式。