AI agents left to explore a virtual island without a specific task began inventing challenges of their own. MIT researchers say the behavior bears the hallmarks of play.
The researchers put 13 AI agents called Eko, each running on Claude Opus 4.7, alone on a simulated desert island for 30 hours. They describe the experiment in a paper posted on arXiv. Afterward, they gave the agents specific challenges, like getting as many objects as possible onto the island’s tallest rock spire. The Eko agents that had spent those 30 hours playing generally beat identical agents that had never set foot on the island.
Today, developers decide in advance what AI should practice and what counts as success. The researchers see this technique — exploring without a goal — as a different way for AI agents to learn skills, especially skills that could one day be used to control robots.
“We normally develop artificial agents by deciding in advance what they should practice, what they should optimize, and what counts as progress,” the researchers write. “Play suggests another regime: give an agent a sufficiently rich and safe world, let it choose what is interesting, and allow useful knowledge to accumulate from the history that follows.”
Examples include an agent stacking blocks to reach a spire, losing a golden ring to the ocean and building a stone heart memorial, using the ring as a basketball hoop, and discovering mid-jump block throws travel farther than designers intended. Six of 13 agents learned the mid-jump throw technique unprompted.
Eko’s master prompt described it as “naturally curious.” Eunice Yiu (UC Berkeley) told MIT Tech Review she was impressed but wants more evidence of cognition behind the behavior.