A hackathon-ready F1-style multi-agent race simulation with AI-driven strategy, pace, and overtaking decisions.

• Lap-by-lap progression with realistic physics
• AI Agent Behavior:
  • Strategy AI → pit-stop decisions, tyre choice
  • Pace AI → push/conserve decisions
  • Overtake AI → probability-based passing
  • Environment AI → weather, track grip
• Pseudo-RL Learning - agents adapt lap-to-lap
• Live Leaderboard with speed, lap times, tyre status
• Frontend Dashboard with:
  • Track animation (Canvas)
  • Live leaderboard
  • Lap time & tyre wear charts (Chart.js)
  • Event log
  • Control panel

Easiest way:

# Double-click start.bat
# Or run:
start.bat

PowerShell:

.\start.ps1

Backend:

cd PitSynapse/backend
pip install -r requirements.txt
python -m uvicorn main:app --reload --port 8000

Frontend:

cd PitSynapse/frontend
npm install
npm run dev

Access:

• Frontend: http://localhost:5173
• Backend API: http://localhost:8000
• API Docs: http://localhost:8000/docs

PitSynapse/
├── backend/              # FastAPI backend
│   ├── main.py          # Server entry point
│   ├── routes/          # API endpoints
│   ├── services/        # Simulation engine
│   ├── models/          # Pydantic models
│   └── requirements.txt # Python dependencies
├── frontend/            # React frontend
│   ├── src/
│   │   ├── components/  # UI components
│   │   ├── pages/     # Pages
│   │   └── services/    # API client
│   └── package.json     # Node dependencies
├── data/                # Agent profiles
├── start.bat            # Windows startup script
├── start.ps1            # PowerShell startup script
└── README.md            # This file

# Test API
python test_api.py

# Test simulation
python test_simulation.py

• GET /health - Health check
• GET /docs - Interactive API documentation
• POST /api/simulate - Run simulation

{
  "race": {
    "total_laps": 10,
    "weather": "dry",
    "track_id": "default"
  },
  "agents": [
    {
      "id": "agent_1",
      "name": "Aggressive Racer",
      "aggression": 0.9,
      "risk_taking": 0.85,
      "tyre_management": 0.4,
      "pit_bias": 0.3
    }
  ]
}

• Backend: FastAPI, Python, Pydantic
• Frontend: React, Vite, Tailwind CSS, Chart.js
• AI: Pseudo-Reinforcement Learning for agent adaptation

MIT

This is a hackathon project. Feel free to fork and improve!

For questions or issues, please open an issue on GitHub.