LLM-Gomoku: A Large Language Model-Based System for Strategic Gomoku with Self-Play and Reinforcement Learning

• URL: http://arxiv.org/abs/2503.21683v1
• Date: Thu, 27 Mar 2025 16:52:25 GMT
• Title: LLM-Gomoku: A Large Language Model-Based System for Strategic Gomoku with Self-Play and Reinforcement Learning
• Authors: Hui Wang,
• Abstract summary: This study aims to develop a Gomoku AI system based on large language models (LLMs)<n>The system is de-signed to understand and apply Gomoku strat-egies and logic to make rational decisions.<n>After extensive self-play training, the model's Gomoku-playing capabilities have been notably enhanced.
• Score: 4.22453895366234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, large language models (LLMs) have shown significant advancements in natural language processing (NLP), with strong capa-bilities in generation, comprehension, and rea-soning. These models have found applications in education, intelligent decision-making, and gaming. However, effectively utilizing LLMs for strategic planning and decision-making in the game of Gomoku remains a challenge. This study aims to develop a Gomoku AI system based on LLMs, simulating the human learning process of playing chess. The system is de-signed to understand and apply Gomoku strat-egies and logic to make rational decisions. The research methods include enabling the model to "read the board," "understand the rules," "select strategies," and "evaluate positions," while en-hancing its abilities through self-play and rein-forcement learning. The results demonstrate that this approach significantly improves the se-lection of move positions, resolves the issue of generating illegal positions, and reduces pro-cess time through parallel position evaluation. After extensive self-play training, the model's Gomoku-playing capabilities have been notably enhanced.

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