Reinforcement Learning Snake Game AI

This project implements a reinforcement learning (RL) agent that learns to play the classic Snake game using a neural network and Q-learning. The project consists of three main components:

1. Game: The implementation of the Snake game with basic logic, rendering, and user input handling.
2. Model: The neural network architecture and the training logic for the RL agent.
3. Agent: The RL agent that interacts with the game, trains the model, and learns from its experience.

Project Structure

• game.py: Contains the SnakeGameAI class, which implements the Snake game logic, including rendering, game rules, and user input.
• model.py: Contains the Linear_QNet class, a simple neural network architecture for Q-learning, and the QTrainer class, which handles training and optimization.
• agent.py: Contains the Agent class, which implements the RL agent, memory management, and action selection.
• helper.py: Contains utility functions for plotting training results, used to visualize scores during training.
• README.md: This file, providing an overview of the project.

Getting Started

To run this project on your local environment, ensure you have Python installed, along with the necessary dependencies like PyTorch, Pygame, and Matplotlib. You can set up a virtual environment to manage dependencies and keep your environment clean.