This is a Gomoku game, with the unique feature of allowing players to duel against the LLM model. Players can compete against the LLM model and experience a Gomoku game powered by deep learning.
- Modify the architecture: change the backend to use WebSocket with Python, and update the frontend to be presented as a web interface using a JavaScript library, such as WGo.js, for the game of Gomoku. Additionally, incorporate a variety of essential features commonly found in web-based game boards.
- Implement custom game settings that allow players to choose the chessboard size, game difficulty, and other preferences.
- Enable save and load functionality (.sgf file save and load, Cache), allowing players to save their game progress at any time and load it later.
- Prepare for fine-tuning, starting with data preparation, using the original GPT-4o model.
- Dataset: https://gomocup.org/results
- Introduce the LLama-3 model, performing continual pretraining with Gomoku knowledge, followed by fine-tuning and RLHF to enhance the gaming experience.
- Enhance the LLM's ability to play Gomoku using Retrieval-Augmented Generation (RAG). Ensure that various board configurations are recorded accurately for precise retrieval.
- Player vs. LLM Model Duel: Players and the LLM model take turns in the Gomoku duel.
- Chessboard Display: Clearly displays the current state of the chessboard.
- Winner Determination: Once a player achieves five in a row, the program automatically declares that player as the winner.
- Install Required Packages: Run
pip install -r requirements.txtto install all necessary Python packages. - Configuration: Ensure you have set the API key in CSconfig_chess.ini.
- Start the Game: Execute main.py to begin the game.
- Gameplay: Players and the LLM model take turns making moves until one side achieves five in a row.
- Game End: The game automatically ends when a player achieves five in a row.
- Interaction with LLM Model: Uses AzureChatOpenAI to interact with the LLM model, obtaining the model's recommended next move.
- Winner Determination: Uses simple loops to check for five in a row horizontally, vertically, and diagonally.
- Chessboard Drawing: Uses Tkinter for chessboard drawing and updates.
prompt = f"""五子棋:
遊戲規則：
兩名玩家輪流在棋盤的空白位置上放置自己的棋子。
第一個玩家使用黑子，第二個玩家使用白子（或相反，根據約定）。
目標是在棋盤上形成一條由五個連續的同色棋子所組成的直線，這條線可以是水平、垂直或斜線。
第一個成功連接五個棋子的玩家獲勝。
策略：
五子棋需要玩家進行策略性思考，以預測對方的下一步，同時防止對方連接五子並嘗試自己連接。進階玩家會有各種開局策略和模式，以及識別危險位置並及時封堵的能力。
你是一位五子棋大師，請根據[當前棋局]及[你的角色]，下出最優的一步解
*如果你看到對方已經有三個以上的子連成一線，請立刻阻止他繼續連成四子或五子*
--> 範例：假設對手是O, 棋盤: 行5, 列3: O, 行5, 列4: O, 行5, 列5: O，這時候你就要下在 行5, 列6 或是 行5, 列2 來阻止他
*以連成5個{mark}為一直線為追求*
--> 範例：假設你是O, 行5, 列2: O, 行5, 列3: O, 行5, 列4: O，行5, 列5: O，這時候你就要下在 行5, 列6 或是 行5, 列1 來連成五子以獲得勝利
*只需要給我一步即可，無論任何情況，就是給我一個答案*
--> 範例：行4, 列3
[當前棋局]:
{board_str}
[你的角色]:
{mark}
輸出格式 *請嚴格按照輸出格式輸出，只能輸出這些，不要輸出其他文字*:
行a, 列b
請指示下一步的行和列:"""
This repository is licensed under the MIT License.