RL-ChessMaster: Chess AI Agent Project

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Project Overview

RL-ChessMaster is a Gomoku AI agent project based on rule-based and reinforcement learning approaches, aiming to explore different game strategies and intelligent decision-making methods.

Core Components

Rule-Based Agent

The rule-based agent's core logic is based on fine-grained board position assessment, primarily involving the following key steps:

Position Evaluation Process

graph TD
    A[Cell Evaluation] --> B{Is Empty Cell?}
    B -->|Yes| C[Calculate Offense Score]
    B -->|No| D[Skip]
    C --> E[Calculate Defense Score]
    E --> F[Weighted Comprehensive Score]
    F --> G[Select Best Position]

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Scoring Mechanism Details

1. Offense Score

   • Analyze continuous chess piece count
   • Identify critical patterns (alive three, alive four, etc.)
   • Consider jump strategies
   • Reward advanced patterns like "double alive three"

2. Defense Score

   • Assess opponent's potential threats
   • Calculate possible opponent patterns
   • Reduce opponent's winning probability

3. Comprehensive Score

   • Combine offense and defense scores
   • Adjustable weights for balance

Key Scoring Factors

• Five in a Row: 100,000 points
• Alive Four: 10,000 points
• Double Alive Three: 5,000 points
• Dead Four: 1,000 points
• Alive Three: 500 points

Main Features

• Multi-dimensional board evaluation
• Flexible weight configuration
• Center position preference
• Jump strategy support

Installation and Usage

git clone https://github.com/your-repo/RL-ChessMaster.git
cd RL-ChessMaster
pip install -r requirements.txt

Contribution Guidelines

1. Fork the project
2. Create a feature branch
3. Commit your code
4. Create a Pull Request

License

MIT License