# OmegaPacman: Interpretable Multi-Agent Learning

- **Event:** [AI Engineer World's Fair Hackathon 2026](https://cerebralvalley.ai/e/aiewf-hackathon-2026)
- **When:** Jun 27 at 9:00 AM – Jun 28 at 5:00 PM (PDT)
- **Where:** San Francisco, CA
- **Team:** [Elden Gu](https://cerebralvalley.ai/u/eg2026)
- **GitHub:** https://github.com/eldengu/omegapacman/tree/antigravity-mag-vertexai-flash
- **Demo video:** https://youtu.be/KxrHJwLWu-Y
- **Gallery:** https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery/34

Four 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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Markdown version of https://cerebralvalley.ai/e/aiewf-hackathon-2026/hackathon/gallery/34. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt