YuriyGuts/snake-ai-reinforcement
📦 Open Source ProjectYuriyGuts
A Deep Reinforcement Learning implementation that trains an AI to play Snake directly from pixel data.
This project provides a complete implementation of a Deep Reinforcement Learning agent designed to play the classic Snake game. Unlike traditional rule-based bots, this agent learns through trial and error using a Deep Q-Network (DQN). The system processes raw screen pixels as input, allowing the neural network to learn spatial relationships and game mechanics autonomously. The repository includes the game environment, the neural network architecture, and the training loop logic. It is built using Python and leverages deep learning libraries to manage the experience replay buffer and target network updates, which are critical for stabilizing the training process in DQN. The code is structured to be modular, making it an ideal starting point for developers looking to experiment with hyperparameters, reward functions, or alternative reinforcement learning algorithms like Double DQN or Dueling DQN.
💡Highlights
- ├─DQN trained from raw pixel data
- ├─Python-based RL implementation
- └─Experience replay buffer included
🎯For
- ├─AI researchers
- ├─Machine learning students
- └─Game developers