  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.
