OmegaPacman: Interpretable Multi-Agent Learning
AI Engineer World's Fair Hackathon 2026Four ghost agents learn to catch a Pac-Man on a grid. After each round, each ghost reflects on what happened and rewrites its strategy — in plain English you can read. What makes this different from black-box multi-agent RL: the agents' entire learned strategy is human-readable text, not neural weights. You can watch a ghost "realize" it should converge from multiple sides — and read the exact note where it wrote that down. We also found something surprising: when we told the ghosts to "cooperate," they passively guarded zones and failed. When we told them only to "catch Pac-Man," cooperation emerged on its own — they began converging and trapping. Coordination wasn't coded; it was discovered. The learning is still unstable — performance varies round to round, and the agents don't yet monotonically improve. But the core result holds: multi-agent strategy that is both emergent and fully interpretable.
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