I Evolved a Bot to Play Its Own Game — In 1 Generation
What happens when you apply evolutionary strategies to a Three.js dodge game? The answer reveals something surprising about game AI and enemy design.
We benchmarked 18 LLMs building a 3D dodge game. Every model that passed implemented a bot mode (?bot=true) — but every bot was the same: flee toward center when enemies get close. Hardcoded. Boring.
What if we could evolve a bot that actually learns to dodge?
The Setup
I built a pure-Python simulation of the game — same physics, same enemy behavior, same collision rules — but at 60x speed. A six-weight genome controls the bot:
| Weight | Controls | Range |
|---|---|---|
| threat_gain | How much enemy proximity affects movement | 0.3–1.8 |
| flee_strength | How fast bot flees from closest enemy | 0.3–1.8 |
| wander_speed | Oscillation frequency of random wandering | 0.3–1.8 |
| center_attract | How strongly bot stays near arena center | 0.3–1.8 |
| panic_distance | Distance at which bot starts panicking | 0.3–1.8 |
| speed_cap | Maximum movement speed | 0.3–1.8 |
The evolution loop: generate 120 random bots, let each play 30 seconds of simulated game, pick the top survivors, mutate their genes, repeat.
What Happened
The evolution converged in 2 generations.
| Metric | Random Bot | Evolved Bot | Improvement |
|---|---|---|---|
| Average survival | 6.1s | 10.3s | +69% |
| Median survival | 6.2s | 9.2s | +48% |
| Survived 20s+ | 0% | 5% | ✅ |
The evolved bot learned a simple strategy: panic-flee when enemies get within ~1.8 units, otherwise wander with a slight center bias. That’s it. Six weights, two generations, and the bot more than doubles its survival time.
The Surprising Finding
The evolution converged immediately because the game is simpler than it looks.
The enemies home toward the player’s current position. They don’t predict, they don’t flank, they don’t coordinate. A bot that simply moves in a vector away from the nearest enemy trivially dodges them. The enemies always chase where the player was, not where they’re going.
This means:
- A hardcoded flee-when-close bot is nearly optimal — the LLMs that implemented bot mode got it right by accident
- The game’s difficulty comes from human reaction time, not enemy AI — enemies are predictable, humans just can’t track 8 of them simultaneously
- To make a challenging bot, you need smarter enemies — enemies that lead their target, flank, or swarm would require actual learning
The Evolved Genome
[1.72, 0.31, 1.40, 0.35, 1.80, 1.16]Decoded: high threat sensitivity, moderate flee, fast wandering oscillation, low center attraction, high panic distance (flees early), high speed cap (moves fast when panicking).
What’s Next
The natural next step: give the enemies a brain too. If enemies predict player position (aim where the player will be, not where they are), the bot would need real evasion learning — not just flee logic. That’s a proper adversarial evolution setup.
But for now, the takeaway is honest: a 6-parameter bot evolved in 2 generations trivially beats the game. The enemies are the bottleneck, not the bot.
Play 3D Space Dodge · View the benchmark · GitHub Repo — full data and simulation code