The Go Transformer: Natural Language Modeling for Game Play
This work provides a novel approach for generating game strategies in Go and potentially over 40 other board games, though it is incremental as it applies an existing language model to a new domain.
The authors tackled the problem of generating strategic moves in Go by fine-tuning GPT-2 on text descriptions of champion games, resulting in a model that produces valid, novel strategies with efficient opening moves favoring corner play over center and side play.
This work applies natural language modeling to generate plausible strategic moves in the ancient game of Go. We train the Generative Pretrained Transformer (GPT-2) to mimic the style of Go champions as archived in Smart Game Format (SGF), which offers a text description of move sequences. The trained model further generates valid but previously unseen strategies for Go. Because GPT-2 preserves punctuation and spacing, the raw output of the text generator provides inputs to game visualization and creative patterns, such as the Sabaki project's game engine using auto-replays. Results demonstrate that language modeling can capture both the sequencing format of championship Go games and their strategic formations. Compared to random game boards, the GPT-2 fine-tuning shows efficient opening move sequences favoring corner play over less advantageous center and side play. Game generation as a language modeling task offers novel approaches to more than 40 other board games where historical text annotation provides training data (e.g., Amazons & Connect 4/6).