Researchers at the Allen Institute for AI (AI2) demonstrated that artificial intelligence agents learned the concept of object permanence — that objects hidden from view are still there — by playing hide and seek.
The agents, playing as both hiders and seekers, learned the game "Cache" via reinforcement learning. The agents began learning about the environment by taking random actions, like pulling on drawers, and dropping objects in random places. Their game play improved as they learned from outcomes, with the hider, for instance, learning that it had selected a good hiding place when the seeker failed to find the object.
Subsequent testing showed that the agents understood the principles of containment and object permanence and were able to rank images based on how much free space they contained.
The agents performed as well or better than models trained on the gold-standard ImageNet.
From IEEE Spectrum
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