Game Development As Human-llm Interaction

Hong Jiale, Wu Hongqiu, Zhao Hai. Arxiv 2024

[Paper] [Code]    
Has Code Reinforcement Learning Training Techniques

Game development is a highly specialized task that relies on a complex game engine powered by complex programming languages, preventing many gaming enthusiasts from handling it. This paper introduces the Interaction-driven Game Engine (IGE) powered by LLM, which allows everyone to develop a custom game using natural language through Human-LLM interaction. To enable an LLM to function as an IGE, we instruct it to perform the following processes in each turn: (1) \(P_{script}\) : configure the game script segment based on the user’s input; (2) \(P_{code}\) : generate the corresponding code snippet based on the game script segment; (3) \(P_{utter}\) : interact with the user, including guidance and feedback. We propose a data synthesis pipeline based on the LLM to generate game script-code pairs and interactions from a few manually crafted seed data. We propose a three-stage progressive training strategy to transfer the dialogue-based LLM to our IGE smoothly. We construct an IGE for poker games as a case study and comprehensively evaluate it from two perspectives: interaction quality and code correctness. The code and data are available at \url{https://github.com/alterego238/IGE}.

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