Summary of The Overcooked Generalisation Challenge, by Constantin Ruhdorfer and Matteo Bortoletto and Anna Penzkofer and Andreas Bulling
The Overcooked Generalisation Challenge
by Constantin Ruhdorfer, Matteo Bortoletto, Anna Penzkofer, Andreas Bulling
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Overcooked Generalisation Challenge (OGC) is a benchmark for studying zero-shot cooperation abilities in agents when faced with novel partners and levels in the Overcooked-AI environment. The challenge interfaces with state-of-the-art dual curriculum design (DCD) methods to generate auto-curricula for training general agents. It’s fully GPU-accelerated, built on minimax, and freely available under an open-source license. Current DCD algorithms struggle to produce useful policies in this novel challenge, even when combined with recent network architectures designed for scalability and generalisability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The OGC is a new way to test how well artificial intelligence (AI) can work with humans in different situations. The goal is to make AI that can learn to cooperate with people who are not like the ones it was trained with. The challenge uses a game called Overcooked, where agents have to work together to complete levels. The agents have to be able to adapt and learn quickly because they don’t know what will happen next. |
Keywords
* Artificial intelligence * Zero shot
