Candles

A small horror game, and a neural network that learned to be it.

Play at candles.shing.in · made by Shreyash · @ShreyashAtX

The idea

You are a small kid in a dark house. Blow out the candle in every room. Something lives here: it is faster than you, it hears you run, and across the table a giant is playing it with a controller, a searchlight and, when it gets impatient, its hand.

That is the game. The experiment, GameNet, asks whether a neural network can be that game.

In GameNet there are no rules. No code moves you, stops you at a wall, opens a door, blows out a candle or decides that you were caught. A transformer looks at this moment of the house and the keys you press, and imagines the next tenth of a second, ten times a second, in your browser. Every wall that stops you is a wall it learned. When it slips, the house slips with it.

The game was written once, as ordinary code, only to have something to learn from. Bots played it about forty thousand times. The network learned from those games, and then the rules were deleted.

	What runs the house	Needs
Classic	The rules, about a thousand lines of code	any computer with a keyboard
GameNet	A neural network with 25 million weights, and nothing else	WebGPU (Chrome or Edge on a computer), a 99 MB download once

In both, the monster is the nightmare:

• a small network that learned to hunt by playing bots;
• it catches 97.5% of them;
• it reaches under beds and into cracks, and squeezes into the vents after you, slower than you crawl.

Winning is rare. That is intended.

How GameNet works

1. Write the game once. The rules (game/src/sim) run headless in Node. Every 50 ms of a game becomes a row of 311 numbers: the kid, the monster and its mind, candles, doors, toys, the searchlight, the hand. Both players' inputs are saved with it.
2. Let bots play it. Kid bots of five styles and varied skill play the monster: 30,000 games, plus 8,000 drills for the rare moments (crawl spaces, the giant's tools). Right: one of those games.
3. Teach a network the next moment. A transformer (384 wide, 12 layers, game/train) reads the row in pieces and the house's tiles, and predicts the next row from the two players' inputs. It is trained first on the recorded games, then on its own output, so it stays stable when it is left to run alone.
4. Teach a monster to hunt. The nightmare (game/src/sim/mind/nightmare.js, game/train/rl) is a 2 × 256 policy trained with PPO against a league of kid bots. Every 0.25 s it picks a goal: the kid, a cut-off, a guess, a noise, a candle to guard, a wardrobe.
5. Delete the rules. In GameNet the world model is the house. The nightmare reads what its monster sees, hears and feels from the model's own output, and presses the monster's buttons. Those buttons go into the model just as your keys do. It runs with ONNX Runtime Web on WebGPU in a worker (game/src/model). A light decoding layer refuses only what the model's own world contradicts, such as a catch with nobody near or a candle relighting.

Where it stands

Measured with bot exams inside the model. The full log, run by run, is in the training spec.

• The mechanics exam: 384 generated moments (every candle, door, wardrobe, toy, crawl space, the searchlight and the hand), each run 3 times. The play model, v5_M_s2u, passes 303 of them every time.
• The hunt: inside the model, the nightmare catches 35 of 40 bot kids.
• Speed: on an Intel Iris Xe laptop it runs at about three-quarters of real time.
• Weak spots: inside the model the searchlight and the hand often miss what the rules would do, and now and then the house slips.
• Cost: all the training took about $17 of rented RTX 4090 time.

Controls

WASD / arrows / stick	run
Shift	creep, quietly
E or Space (hold)	blow out a candle
E or Space (tap)	open and shut doors, hide in and leave wardrobes
Q	throw the toy you carry (it makes a noise where it lands)
mouse wheel	camera closer / further
Esc	pause, and the menu
M / H	sound / hints

For developers

Run it

cd game
npm install
npm run dev        # http://localhost:5190

npm run build writes game/dist (about 134 MB): the game, the audio, the play model and the runtime's files.

Deploy

game/dist is static. The host must send two headers so the runtime can use threads:

Cross-Origin-Opener-Policy: same-origin
Cross-Origin-Embedder-Policy: require-corp

• Vercel: import the repo with Root Directory game. game/vercel.json sets the build, the headers and the caching.
• Other hosts: set the same two headers.
• File sizes: the largest file is the runtime's WebAssembly (26.8 MB). Cloudflare Pages caps files at 25 MiB, so use Vercel or Netlify, or another host without that cap.
• The model: it ships as game/public/models/v5_M_s2u/play.json plus four 24 MB parts (node tools/ship_model.mjs NAME makes them from a trained packed_brain.onnx). The browser keeps the parts by their md5, so a return visit skips the download.
• Models hosted elsewhere: build with VITE_MODEL_BASE=https://…/models/. That host must allow CORS.

This is the beginning of the README. Read the rest at the original. Read the full README ↗