Formula-1-Performance-Optimizer

🏁 F1 Performance Optimizer — Reinforcement Learning Meets Racing

Ever wondered how AI could take on the role of a Formula 1 race strategist? This project explores exactly that.

Using reinforcement learning, I built an intelligent agent that learns how to make better race decisions — like when to pit, how to manage tires, and how to balance fuel and speed — all within a custom-built F1 simulation.

The entire project was developed in Google Colab, making experimentation fast, collaborative, and GPU-powered.

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🚀 What This Project Does

• 🎯 Teaches an AI Agent to Drive Smarter
  It uses the Proximal Policy Optimization (PPO) algorithm to help the agent learn what works best on the track.

• 🧪 Tunes It for Peak Performance
  With Optuna doing the heavy lifting on hyperparameter optimization, the model gets smarter, faster.

• 🛞 Simulates Realistic Race Conditions
  Think tire wear, pit stops, fuel usage, lap times — all modeled to mimic a real F1 race environment.

• 🧠 Focuses on Strategy, Not Just Speed
  The goal isn’t just to go fast — it's to go smart. The agent learns how to optimize strategy over an entire race.

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⚙️ How It Works (in plain English)

1. Build the Track
   First, I created a simulation that mimics an F1 race — tracks, cars, tires, pit stops, and all.

2. Train the Brain
   Using PPO from the Stable-Baselines3 library, the agent races again and again, learning from trial and error.

3. Tweak Until It's Sharp
   With Optuna, the model’s parameters are automatically adjusted for better performance.

4. Test and Analyze
   Finally, I evaluate the agent based on lap times, tire health, and overall race strategy. It’s not about brute force — it’s about precision.

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🛠️ Tech Stack

• Python – the language of choice
• Google Colab – for fast, GPU-backed training and easy sharing
• Stable-Baselines3 – reinforcement learning library (PPO algorithm)
• Optuna – for hyperparameter tuning
• OpenAI Gym – environment framework, customized for F1
• PyTorch – under the hood of the learning models
• NumPy, Pandas, Matplotlib – data handling and visualization

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🧩 Why This Matters

In real F1, milliseconds matter. Teams make dozens of strategic decisions every race. This project shows how an AI can be trained to make some of those calls — and possibly do it better than a human under pressure.

Whether you're into racing, AI, or just love a cool project — I hope you enjoy digging into this as much as I enjoyed building it.

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Feel free to explore, experiment, or even race your own agent!