论文标题
国际象棋变压器：使用生成语言模型掌握游戏
The Chess Transformer: Mastering Play using Generative Language Models
论文作者
论文摘要
这项工作表明，自然语言变形金刚可以支持更通用的战略建模，尤其是对于文本席位的游戏。除了学习自然语言技能外，抽象的变压器体系结构还可以在棋盘上产生有意义的动作。通过进一步的微调，Transformer通过在便携式游戏符号中对280万个国际象棋游戏进行训练来学习复杂的游戏玩法。经过30,000个训练步骤，OpenAI的生成预培训变压器（GPT-2）优化了7.74亿个参数的权重。这种微调的国际象棋变压器生成了合理的策略，并显示出可以识别为经典开口（例如英语或斯拉夫交易所）的游戏编队。最后，在现场播放中，新颖的模型展示了一个人与人之间的界面，该界面正确过滤了非法动作，并提供了一种挑战变压器国际象棋策略的新方法。我们预计将来的工作将基于该变压器的承诺，尤其是在其他策略游戏中，功能可以从简单但表现力的播放器注释中捕获基本的复杂规则语法。
This work demonstrates that natural language transformers can support more generic strategic modeling, particularly for text-archived games. In addition to learning natural language skills, the abstract transformer architecture can generate meaningful moves on a chessboard. With further fine-tuning, the transformer learns complex gameplay by training on 2.8 million chess games in Portable Game Notation. After 30,000 training steps, OpenAI's Generative Pre-trained Transformer (GPT-2) optimizes weights for 774 million parameters. This fine-tuned Chess Transformer generates plausible strategies and displays game formations identifiable as classic openings, such as English or the Slav Exchange. Finally, in live play, the novel model demonstrates a human-to-transformer interface that correctly filters illegal moves and provides a novel method to challenge the transformer's chess strategies. We anticipate future work will build on this transformer's promise, particularly in other strategy games where features can capture the underlying complex rule syntax from simple but expressive player annotations.
