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.

本篇论文介绍了一种创新方法，PCGPT 框架，该方法利用离线强化学习和 Transformer 网络进行程序化内容生成（PCG）。PCGPT 利用基于 Transformer 的自回归模型迭代生成游戏关卡，解决了传统 PCG 方法中重复、可预测或不一致的内容的挑战。该框架模拟动作、状态和奖励的轨迹，利用 Transformer 的自注意机制捕捉时间依赖性和因果关系。该方法在 Sokoban 益智游戏中进行了评估，模型预测了所需物品及其对应位置。Sokoban 游戏的实验结果表明，PCGPT 生成了更复杂和多样化的游戏内容。有趣的是，与现有方法相比，PCGPT 在显著较少的步骤中实现了这些结果，展示了其增强游戏设计和在线内容生成的潜力。我们的模型代表了一种超越以往方法的新的 PCG 范例。