PCGPT：基于Transformer的程序化内容生成
PCGPT: Procedural Content Generation via Transformers
AI总结：
针对传统程序化内容生成方法内容重复、一致性差等问题，提出结合离线强化学习与Transformer的PCGPT框架，通过自注意力机制建模轨迹生成游戏内容，在Sokoban游戏上以更少步骤生成更复杂多样的内容，优于现有方法。
摘要：
本文提出PCGPT框架，这是一种结合离线强化学习与Transformer网络的创新程序化内容生成（PCG）方法。PCGPT采用基于Transformer的自回归模型迭代生成游戏关卡，解决了传统PCG方法存在的内容重复、可预测性强或一致性差的难题。该框架对动作、状态和奖励的轨迹进行建模，利用Transformer的自注意力机制捕捉时间依赖关系与因果关系。研究在Sokoban（推箱子）益智游戏中对该方法进行评估，模型会预测所需道具及其对应位置。在Sokoban游戏上的实验结果表明，PCGPT能生成更复杂、更多样的游戏内容。值得注意的是，与现有方法相比，它以显著更少的步骤取得了这些结果，展现出在优化游戏设计和在线内容生成方面的潜力。我们的模型代表了一种新的PCG范式，性能优于以往方法。
英文摘要：
The paper presents the PCGPT framework, an innovative approach to procedural content generation (PCG) using offline reinforcement learning and transformer networks. PCGPT utilizes an autoregressive model based on transformers to generate game levels iteratively, addressing the challenges of traditional PCG methods such as repetitive, predictable, or inconsistent content. The framework models trajectories of actions, states, and rewards, leveraging the transformer's self-attention mechanism to capture temporal dependencies and causal relationships. The approach is evaluated in the Sokoban puzzle game, where the model predicts items that are needed with their corresponding locations. Experimental results on the game Sokoban demonstrate that PCGPT generates more complex and diverse game content. Interestingly, it achieves these results in significantly fewer steps compared to existing methods, showcasing its potential for enhancing game design and online content generation. Our model represents a new PCG paradigm which outperforms previous methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
