Flappy Bird AI

This project implements an AI to play the Flappy Bird game using the NEAT (NeuroEvolution of Augmenting Topologies) algorithm. The AI learns to navigate between pipes and achieve higher scores through evolution over generations.

📌 Table of Contents

• Showcase
• Motivation
• Project Structure
• Requirements
• Installation
• Usage
  • Running the Game (Playable)
  • Training the AI (Non-playable)
  • Visualizing Training Progress
• Game Elements
  • Bird
  • Pipe
  • Base
  • Score
• NEAT Algorithm Overview
• License
• Acknowledgements

Showcase

NEAT Playing Flappy Bird:

🎯 Motivation

This project was created as an exploration of the NEAT algorithm and its applications in reinforcement learning.

📁 Project Structure

Files and Directories

• .gitattributes: Configuration file for Git attributes.
• LICENSE: MIT License for the project.
• README.md: This file.
• media/: Directory containing media files such as images and models.
• models/: Directory containing trained models and fitness graphs.
• Project/: Main project directory containing the game and AI implementation.
  • neat_run.py: Script to run the NEAT algorithm and train the AI.
  • neat.config.txt: Configuration file for the NEAT algorithm.
  • run.py: Script to run the Flappy Bird game manually.
  • visualize.py: Script to visualize training progress with graphs.
  • assets/: Directory containing game assets like images and audio.
  • game/: Directory containing game logic and classes.

🔍 Requirements

• Python 3.8.1 or above
• Pygame
• NEAT-Python
• Matplotlib

💻 Installation

1. Clone the repository:

    git clone https://github.com/yourusername/flappy-bird-ai.git
    cd flappy-bird-ai

2. Install the required packages:

    pip install pygame neat-python matplotlib

🚀 Usage

🔥 Running the Game (Playable)

To run the Flappy Bird game manually:

python run.py

🧩 Training the AI (Non-playable)

To train the AI using the NEAT algorithm:

python neat_run.py

When the training is completed, the best model will be saved in the models/ directory.

📊 Visualizing Training Progress

To generate graphs showing training progress:

python visualize.py

The graphs are saved in the models/ directory.

🎮 Game Elements

🐦 Bird

Responsible for the bird's movement, jumping, and collision detection.

🟢 Pipe

Controls the pipes' movement and handles collisions with the bird.

🟫 Base

Manages the moving base at the bottom of the screen.

🔢 Score

Tracks and renders the score on the screen.

🧠 NEAT Algorithm Overview

NEAT (NeuroEvolution of Augmenting Topologies) is a powerful AI algorithm that evolves neural networks using reinforcement learning. The process involves:

• Creating an initial population of birds, each with a unique genome and neural network.
• Evaluating birds based on survival time and pipe passages.
• Selecting top-performing birds, breeding them, and applying mutations.
• Repeating over generations to improve performance.

For more information, see the NEAT documentation.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

💬 Acknowledgements

• The NEAT-Python library for implementing the NEAT algorithm.
• The Pygame library for creating the game.
• The original Flappy Bird game for inspiration.