  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.
