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Representing a board game and its positions by text-based notation enablesthe possibility of NLP applications. Language models, can help gain insightinto a variety of interesting problems such as unsupervised learning rules of agame, detecting player behavior patterns, player attribution, and ultimatelylearning the game to beat state of the art. In this study, we applied BERTmodels, first to the simple Nim game to analyze its performance in the presenceof noise in a setup of a few-shot learning architecture. We analyzed the modelperformance via three virtual players, namely Nim Guru, Random player, andQ-learner. In the second part, we applied the game learning language model tothe chess game, and a large set of grandmaster games with exhaustiveencyclopedia openings. Finally, we have shown that model practically learns therules of the chess game and can survive games against Stockfish at a category-Arating level.

Learning Chess With Language Models and Transformers