🐍 SUPERSNAKE – AI-Powered Reinforcement Learning

SUPERSNAKE is an super advanced Snake AI using reinforcement learning. Through Proximal policy optimization(PPO) and strategic reward providing, the SUPERSNAKE learns to navigate, grow, and maximize its score while avoiding collisions and randomized booms. Our goal is to develop an efficient agent that develop regular Snake into SUPER-SNAKE beyond a superhuman level.

• 📂 Source Code: View on GitHub

🔗 Project Links

📄 Project Reports: - Proposal - Status - Final

🖼️ Screenshots & Media

A sample screenshot of SUPERSNAKE in action.

🔍 References & Resources

• Reinforcement Learning: Proximal policy optimization(PPO)
• Libraries Used: TensorFlow/PyTorch, OpenAI Gymnasium, NumPy, Pygame, Matplotlib
• Training Approach: Reward based optimization
• Game Mechanics: Implemented using pygame, featuring randomized obstacles and dynamic environments(for future version)