用自然语言指导强化学习生成游戏关卡，控性强且泛化好。
IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generation
- 基于文本指令的强化学习方法，压缩关卡条件信息
- 控性提升21.4%，未见指令泛化能力提升17.2%
- 支持灵活文本输入，适合游戏设计与内容生成研究者
近期研究强调了自然语言在提升生成模型可控性方面的价值。尽管已有多种利用自然语言进行内容生成的方法，但将文本指令用于深度强化学习（DRL）代理进行程序化内容生成的研究仍较少。本文提出IPCGRL，一种基于指令的程序化关卡生成方法，结合句子嵌入模型。IPCGRL微调特定任务的嵌入表示，有效压缩游戏关卡条件。我们在二维关卡生成任务中评估IPCGRL，与通用嵌入方法对比，结果表明其在控性上最高提升21.4%，在未见指令的泛化能力上提升17.2%。此外，该方法扩展了条件输入的模态，为程序化内容生成提供了更灵活、更具表现力的交互框架。
原文摘要 · Abstract (English)
Recent research has highlighted the significance of natural language in enhancing the controllability of generative models. While various efforts have been made to leverage natural language for content generation, research on deep reinforcement learning (DRL) agents utilizing text-based instructions for procedural content generation remains limited. In this paper, we propose IPCGRL, an instruction-based procedural content generation method via reinforcement learning, which incorporates a sentence embedding model. IPCGRL fine-tunes task-specific embedding representations to effectively compress game-level conditions. We evaluate IPCGRL in a two-dimensional level generation task and compare its performance with a general-purpose embedding method. The results indicate that IPCGRL achieves up to a 21.4% improvement in controllability and a 17.2% improvement in generalizability for unseen instructions. Furthermore, the proposed method extends the modality of conditional input, enabling a more flexible and expressive interaction framework for procedural content generation.
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