AI-Powered Snake Game 🐍

This project is a complete implementation of the classic Snake game, built from scratch using Python and Pygame. The primary goal is to train an Artificial Intelligence (AI) agent using Deep Q-Learning (DQN) to play the game autonomously and achieve the highest possible score.

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✨ Features

• Complete Snake Game: Built from the ground up with Pygame, including all core game mechanics.
• DQN Agent: A Reinforcement Learning agent that learns from experience to master the game.
• Advanced State Representation: The agent uses a 19-feature state vector for broad spatial awareness, including immediate dangers, a map of its surroundings, and food location.
• Experience Replay: Implements a memory buffer to store and sample past experiences, stabilizing the learning process.
• Live Training Visualization: The training loop is accompanied by a real-time plot to monitor scores and their moving average.
• Optimized for Speed: The training loop is decoupled from human-viewable speed, allowing the agent to learn rapidly.

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🛠️ Tech Stack

• Python 3
• Pygame for the game engine and graphics.
• PyTorch for building and training the neural network model.
• Matplotlib & IPython for live data visualization.
• NumPy for state representation.

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🚀 Setup and Installation

To run this project on your local machine, follow these steps:

1. Clone the Repository

   git clone [https://github.com/username/RL-Snake-Pygame.git](https://github.com/username/RL-Snake-Pygame.git)
   cd RL-Snake-Pygame

   (Replace username with your GitHub username.)

2. Create and Activate a Virtual Environment

   # Create the venv
   python -m venv venv
   
   # Activate on Windows
   .\venv\Scripts\activate
   
   # Activate on macOS/Linux
   source venv/bin/activate

3. Install Dependencies Use the requirements.txt file to install all necessary libraries.

   pip install -r requirements.txt

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🎮 How to Run

The project has two execution modes:

1. Train the AI Agent (Default Mode) Run the agent.py file to start the training process.

   python agent.py

   You will see the game window where the AI plays at high speed, and a plot will appear to track its progress.

2. Play as a Human If you want to try the game yourself, run the game.py file.

   python game.py

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📂 Project Structure

.
├── agent.py        # Main file for training the AI agent
├── game.py         # Core logic and engine for the Snake game
├── model.py        # Neural Network (Linear_QNet) and Trainer (QTrainer) definitions
├── plot.py         # Script for plotting visualization
├── requirements.txt # List of project dependencies
├── LICENSE         # MIT License
└── README.md       # This documentation