BeamNG.Earth
BeamNG.drive용 1:1 비율 실시간 지구, 실제 고도·위성 이미지·부동 원점·보조 서버에서 LOD 스트리밍
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A kids' game about neural networks. Drive a race car yourself, then build brains that learn to drive by evolution, level by level. Tracks: a home track plus simplified shapes inspired by Monza, Monaco, Silverstone, Spa-Francorchamps, Suzuka (with its bridge), Interlagos, Montréal, Red Bull Ring, Hungaroring and Zandvoort. Each sketch in src/sim/track.ts is scaled to roughly the real lap length, then smoothed and grown until no corner is too tight and the road never overlaps (checked in track.test.ts).README 전문 보기 ↗
A kids' game about neural networks. Drive a race car yourself, then build brains that learn to drive by evolution, level by level.
| Level | What you learn | Network |
|---|---|---|
| 1. You drive | The 7 numbers a car can feel | your brain |
| 2. Two perceptrons | Weights, bias, the "squeeze"; tune a brain by hand | 7 → 2 |
| 3. One hidden layer | Hidden neurons make "this and that" possible | 7 → 8 → 2 |
| 4. Two hidden layers | Ideas built on ideas; 434 weights | 7 → 16 → 16 → 2 |
| 5. Build your own | Pick layers and neurons, compare shapes | you decide |
| 6. Corner sense | Feature engineering: give the car new inputs | 9 → 16 → 16 → 2 |
| 7. Ray designer | How many sensor rays, at what angles, how long | rays + 2 → 16 → 16 → 2 |
| 8. Turn off a sense | Ablation: which inputs really matter | fewer inputs |
| 9. Smarter inputs | Derived features: time to crash, brake distance, lane | 7 + clues |
| 10. New tracks | Overfitting vs generalising, on famous F1 circuits | train on 1–4 tracks, exam on all |
| 11. Noisy sensors | Robustness: training with noise | 7 → 16 → 16 → 2 |
| 12. Memory | Feeding the last outputs back in (recurrence) | 9 → 16 → 16 → 2 |
| 13. Reward design | What "best" means, and reward hacking | 7 → 16 → 16 → 2 |
| 14. Learn from you | Supervised learning with backpropagation | 7 → 16 → 2 (copycat) |
Tracks: a home track plus simplified shapes inspired by Monza, Monaco, Silverstone, Spa-Francorchamps, Suzuka (with its bridge), Interlagos, Montréal, Red Bull Ring, Hungaroring and Zandvoort. Each sketch in src/sim/track.ts is scaled to roughly the real lap length, then smoothed and grown until no corner is too tight and the road never overlaps (checked in track.test.ts).
npm install
npm run dev # http://localhost:5173
npm test # simulation unit tests
npm run build # static site in dist/ (works from any folder, e.g. GitHub Pages)
npm run build:artifact # one self-contained HTML file in dist-artifact/
npm run narrate # record new or changed narration lines with ElevenLabs (needs ELEVENLABS_API_KEY in .env)
src/sim/ is the simulation, with no React or three.js: track, car physics and sensors, the neural network, race scoring, and the evolution Lab (one brain at a time, or 20 at once).src/render/Track3D.ts draws the 3D track with three.js from a SceneState.src/components/ holds the shared UI: track stage, network diagram, lab panels, charts.src/levels/ has one file per level; src/levels.ts lists them for the home screen.src/narration/ is the coach: script.ts holds what it says (an intro per level, and a wrap-up that plays before "Next level"), each line spotlighting an element marked data-tour="...". audio/ holds one mp3 per line, recorded by scripts/narrate.mjs.Every test drive is a 10-lap race. Brains are ranked by distance until they finish all 10 laps, then by total race time.