Multi-Agent Connect Four Game

Developer

Project by: Abhishek Raghuraman

A project showcasing modern game development techniques, multi-agent AI implementation, and professional UI design in Python.

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Project Description

Objectives

Multi-Agent Connect Four is a modern implementation of the classic Connect Four board game, developed as a comprehensive demonstration of game development skills and AI programming techniques.

Primary Objectives:

• Create a fully functional Connect Four game with intuitive mouse-based controls
• Implement intelligent AI opponents with multiple difficulty levels and distinct strategies
• Develop a modern, visually appealing user interface with smooth animations and dark theme
• Provide comprehensive game statistics and performance tracking

Scope

This project encompasses a complete desktop gaming application featuring:

Core Game Features:

• Human vs Human - Classic two-player local multiplayer mode
• Human vs AI - Single-player mode with selectable AI difficulty levels
• AI vs AI Demo - Automated demonstration showing AI strategies in action
• Real-time winner detection with visual highlighting of winning pieces
• Animated gameplay with smooth piece dropping and visual feedback
• Game statistics tracking with persistent data storage

AI Implementation:

• Easy AI - Random move selection with slight center column preference
• Medium AI - Strategic gameplay featuring win detection, opponent blocking, and center positioning
• Hard AI - Advanced strategic evaluation with multi-move planning and threat analysis
• Automatic AI moves with realistic thinking delays and visual indicators

User Interface:

• Modern dark theme with professional color scheme and typography
• Responsive button system with hover effects and visual feedback
• Statistics dashboard displaying game history, win rates, and performance metrics
• Game over screens with detailed match results and replay options
• Smooth navigation between all game screens and modes

Technical Features:

• Modular architecture with clean separation of concerns
• Robust error handling and input validation
• Data persistence using JSON for game statistics

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Tools and Libraries Used

Core Technologies

• Python 3.8+ - Primary programming language for all game logic and AI implementation
• Pygame 2.1+ - Graphics rendering engine, event handling, and game loop management
• JSON - Built-in library for data serialization and statistics persistence

Architecture and Design

• Object-Oriented Programming - Clean class-based architecture with inheritance
• Modular Design Pattern - Separated concerns across multiple specialized modules
• Type Hints - Enhanced code documentation and IDE support using typing module

File Structure and Components

ConnectFour/
├── main.py                   # Application entry point
├── requirements.txt          # Project dependencies specification
├── src/                      # Source code directory
│   ├── __init__.py           # Package initialization
│   ├── config.py             # Game configuration and visual constants
│   ├── board.py              # Game board logic and rendering
│   ├── player.py             # Player classes (Human and AI)
│   ├── game_engine.py        # Main game engine and state management
│   └── ui_manager.py         # User interface management and rendering
└── data/                     # Runtime data storage
    ├── scores.json           # Game results and match history

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Setup Instructions

Prerequisites

Ensure your system has the following installed:

• Python 3.8 or higher
• pip

Installation Steps

1. Download and Extract Project

Ensure you have the complete project directory with all source files:

• main.py - Application entry point
• src/ - Directory with all Python modules
• data/ - Directory for game statistics
• requirements.txt - File which specifies the project's dependencies

# Install pygame directly
pip install requirements.txt

# Run the game
python main.py

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Game Controls and Features

Basic Controls

• Mouse Movement - Hover over board columns to preview piece placement
• Left Mouse Click - Drop piece into selected column
• ESC Key - Return to main menu from any screen or game state
• Menu Navigation - Click buttons to navigate between different screens

Game Modes Available

1. Human vs Human

• Local Multiplayer - Two players alternate turns on the same computer
• Turn Indicators - Clear visual indication of whose turn it is
• Win Detection - Automatic detection of horizontal, vertical, and diagonal wins
• Statistics Tracking - All games recorded for performance analysis

2. Human vs AI

• Difficulty Selection - Choose from Easy, Medium, or Hard AI opponents
• AI Thinking Indicators - Visual feedback showing when AI is calculating moves
• Strategic Gameplay - Each difficulty level employs different strategies:
  • Easy: Random moves with slight center preference
  • Medium: Win/block detection with strategic positioning
  • Hard: Advanced move evaluation and multi-turn planning

3. AI vs AI Demo

• Automated Gameplay - Watch AI opponents battle each other
• Strategy Comparison - Observe different AI difficulty levels competing
• Educational Value - Learn Connect Four strategy by watching AI play

Statistics and Data Management

• Comprehensive Tracking - All games automatically recorded with detailed statistics
• Performance Metrics - Win rates, average game duration, and move analysis
• Persistent Storage - Game data saved automatically between sessions
• Game History - Review recent matches with complete game details

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Technical Implementation

Architecture Overview

The project follows a clean, modular architecture with well-separated concerns:

• main.py - Application entry point with error handling and pygame initialization
• config.py - Centralized configuration management for all game constants
• board.py - Core game logic including move validation and winner detection
• player.py - Player abstraction with Human and AI implementations
• game_engine.py - Main game state management and flow control
• ui_manager.py - Complete user interface rendering and event handling

AI Implementation Details

The AI system features three distinct difficulty levels:

Easy AI:

• Random move selection from valid positions
• Basic center column preference for improved positioning
• Minimal strategic thinking for beginner-friendly gameplay

Medium AI:

• Immediate win detection and execution
• Opponent threat blocking to prevent losses
• Strategic center column positioning
• Balanced challenge for intermediate players

Hard AI:

• Advanced move evaluation with scoring system
• Multi-move strategic planning
• Threat analysis and tactical positioning
• Challenging gameplay for experienced players

Performance Characteristics

• Responsive AI - Move calculations complete within 0.5-1.5 seconds
• Efficient Rendering - Optimized graphics for smooth visual experience
• Minimal Memory Usage - Lightweight design suitable for all systems

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Configuration and Customization

Game Settings

Modify src/config.py to customize game parameters:

# Board Configuration
ROWS = 6                    # Number of board rows
COLS = 7                    # Number of board columns
CONNECT_LENGTH = 4          # Pieces needed to win

# Display Settings
WINDOW_WIDTH = 900          # Game window width
WINDOW_HEIGHT = 700         # Game window height
CELL_SIZE = 80              # Size of each board cell
PIECE_RADIUS = 35           # Radius of game pieces

AI Difficulty Adjustment

Modify AI thinking times and behaviors in src/player.py:

# Thinking delays
self.think_time = {
    'EASY': 0.5,            # Fast random moves
    'MEDIUM': 1.0,          # Moderate strategic thinking
    'HARD': 1.5             # Longer strategic analysis
}.get(difficulty, 1.0)

Features and Gameplay

Winning Conditions

• Horizontal - Four pieces in a row horizontally
• Vertical - Four pieces in a column vertically
• Diagonal - Four pieces diagonally (both directions)
• Draw - Board completely filled with no winner

Visual Feedback

• Piece Preview - Hover over columns to see where pieces will drop
• Turn Indicators - Clear display of current player and colored indicators
• Winning Highlights - Animated effect on winning piece combinations
• AI Status - Visual indication when AI is calculating moves

Statistics Dashboard

• Game History - Complete record of all matches played
• Win Statistics - Breakdown of wins by player type (Human/AI/Draw)
• Performance Metrics - Average game duration and move counts
• Recent Games - Quick view of last 5 games with detailed results

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Development Notes

Code Quality

• Clean Architecture - Well-organized modules with single responsibilities
• Type Safety - Comprehensive type hints throughout the codebase
• Error Handling - Robust exception handling for stability
• Documentation - Clear docstrings and comments explaining functionality

Extensibility

The modular design allows for easy extension:

• Additional AI Types - Extend AIPlayer class for new strategies
• UI Themes - Modify color schemes in config.py
• New Game Modes - Add functionality to game_engine.py
• Enhanced Statistics - Extend data tracking in ui_manager.py

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Technical Specifications:

• Minimum Requirements: Python 3.8, Pygame 2.1, 2GB RAM
• Recommended: Python 3.10+, 4GB RAM for optimal performance
• Platform Support: Any platform supporting Python and Pygame