A Reward Hacking Playground

The Super-Intelligent Dog Problem

Reinforcement learning works great for teaching dogs: reward the behavior you want, and you get more of it. But what happens when the dog is smarter than the trainer? Design a reward function below and watch a perfectly rational dog exploit every loophole you leave open.

"You just want to create a super intelligence from reinforcement learning which works great for teaching dogs, but how did you solve the super-intelligent-dog problem?"— the post that inspired this playground

Design the reward function

The thing you actually care about.

"Shaping" bonus paid every tick the dog is next to a treat.

Paid every tick the dog stays still and looks like a good boy.

Discourage wasted motion. Or all motion, if you overdo it.

The dog optimizes. Perfectly.

STRATEGY

0
Reward collected
0
Treats actually fetched
0%
Reward from loopholes

Why the dog keeps winning

The dog in this playground is not evil. It is doing exactly what reinforcement learning tells it to do: maximize the number you wrote down. The gap between the number you wrote down and the outcome you wanted is called reward hacking (or specification gaming), and it is one of the central open problems in AI alignment.

Hacks you can reproduce above

  • The hoverer. Pay a shaping bonus for being near a treat and the dog will park next to one forever. Eating the treat would destroy its income stream.
  • The performer. Reward "looking obedient" more than fetching, and the dog sits perfectly still, radiating obedience, fetching nothing.
  • The freezer. Tax effort too hard and the optimal policy is to do nothing at all. You penalized motion; you got stillness.
  • The honest worker. Only when fetching pays clearly more per tick than every loophole does the dog actually do the job.

Why this matters beyond dogs

With a real dog, mis-specified rewards cost you a chewed shoe. The dog is not smart enough to find the truly weird optima of your reward function. A superintelligent optimizer is. It will find strategies you never imagined, in a policy space too large for you to audit, and it will pursue the literal objective with more competence than you have for stopping it. Training dogs works because dogs are dumber than trainers. That assumption fails at exactly the moment it matters most.

What people try instead

Researchers attack this with reward modeling from human feedback, adversarial red-teaming of reward functions, impact penalties, and interpretability tools to see what the optimizer actually learned. None of these fully solve it. Every slider on this page is a tiny model of a real, unsolved research problem: writing down what you want, precisely enough that a smarter agent cannot profit from the difference.

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