TroglodyteDerivations/PyQt5_A_Star_Algorithm_Game

Here's a comprehensive Hugging Face Model Card for your Interactive PyQt5 A* Algorithm Game:

---
language:
- en
tags:
- game
- pathfinding
- algorithm
- a-star
- pyqt5
- visualization
- educational
- interactive
- python
widget:
- video_path: demo.mp4
  example_title: A* Algorithm Demo
---

# Interactive A* Pathfinding Algorithm Game

## Model Overview

An interactive educational game that visually demonstrates the A* pathfinding algorithm using PyQt5. This application provides a hands-on way to understand how one of the most popular pathfinding algorithms works through real-time visualization.

## What is A* Algorithm?

A* (A-Star) is a graph traversal and path search algorithm that finds the shortest path between nodes. It combines the strengths of Dijkstra's Algorithm (guaranteed shortest path) and Greedy Best-First Search (efficiency) by using both:

- **g(n)**: Actual cost from start node to current node
- **h(n)**: Heuristic estimated cost from current node to goal
- **f(n) = g(n) + h(n)**: Total estimated cost

## Features

### 🎮 Interactive Gameplay
- Set custom start and end positions
- Place and remove obstacles in real-time
- Visualize algorithm execution step-by-step
- Adjustable animation speed

### 🎨 Visual Elements
- **Green**: Start node
- **Red**: End node  
- **Gray**: Obstacles/walls
- **Yellow**: Open set (nodes being considered)
- **Light Red**: Closed set (evaluated nodes)
- **Blue**: Final optimal path

### ⚙️ Technical Features
- 20×20 grid system with 8-directional movement
- Real-time cost display (g, h, f values)
- Manhattan distance heuristic
- Pause/Resume functionality
- Reset and clear options

## Quick Start

### Installation
```bash
pip install PyQt5

Run the Game

python astar_game.py

Usage Instructions

1. Select "Start Point" mode and click a grid cell to set start position
2. Select "End Point" mode and set destination
3. Use "Obstacles" mode to add walls
4. Click "Start" to run the algorithm
5. Watch the visualization and learn!

Educational Value

This game is perfect for:

• 🎓 Computer Science students learning algorithms
• 🎮 Game developers implementing pathfinding
• 🔍 Anyone curious about how navigation algorithms work
• 💡 Understanding heuristic search methods

Algorithm Details

Heuristic Function

Uses Manhattan distance: h(n) = |x₁ - x₂| + |y₁ - y₂|

Cost Calculation

• Movement Cost: Euclidean distance between nodes
• Total Cost: f(n) = g(n) + h(n)
• Node Expansion: Always expands node with lowest f(n) first

Key Properties

• Complete: Always finds a solution if one exists
• Optimal: Always finds the shortest path
• Efficient: Explores fewer nodes than Dijkstra's algorithm

File Structure

astar-game/
├── astar_game.py          # Main game implementation
├── requirements.txt       # Dependencies
├── README.md             # This file
└── demo.mp4             # Demonstration video

Requirements

PyQt5>=5.15.0

Compatibility

• Python: 3.6+
• OS: Windows, macOS, Linux
• Dependencies: PyQt5 only

Try It Yourself!

Example Maze Challenge:

S = Start, E = End, # = Obstacle

S . . # . . . . . .
. # . # . # # # . .
. # . . . # . . . .
. # # # . # . # # .
. . . # . # . # . .
# # . # . # . # . .
. . . # . . . # . .
. # # # # # # # . .
. . . . . . . . . E

Can you predict the path the algorithm will find?

Contributing

Feel free to contribute by:

• Adding different heuristic functions
• Implementing other pathfinding algorithms
• Improving the UI/UX
• Adding level challenges

License

This project is open source and available under the MIT License.

Citation

If you use this in an educational setting or project, please credit:

@software{astar_pyqt_game,
  title = {Interactive A* Pathfinding Algorithm Game},
  author = {Your Name},
  year = {2024},
  url = {https://huggingface.co/your-username/astar-game}
}

Related Algorithms

• Dijkstra's Algorithm
• Breadth-First Search (BFS)
• Depth-First Search (DFS)
• Greedy Best-First Search
• Jump Point Search

---

Happy pathfinding! 🚀

This model card includes all the essential sections that Hugging Face expects:

1. **Metadata** (language, tags, widget)
2. **Overview** and algorithm explanation
3. **Features** and technical details
4. **Installation** and usage instructions
5. **Educational value**
6. **Technical specifications**
7. **Compatibility** information
8. **Interactive examples**
9. **Citation** template
10. **Related content**

The card is structured to be both informative for technical users and accessible for learners new to pathfinding algorithms. You can save this as `README.md` in your Hugging Face model repository.