Summary of Noisy Zero-shot Coordination: Breaking the Common Knowledge Assumption in Zero-shot Coordination Games, by Usman Anwar et al.
Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination Games
by Usman Anwar, Ashish Pandian, Jia Wan, David Krueger, Jakob Foerster
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers explore a new setting for studying reinforcement learning (RL) agent coordination called noisy zero-shot coordination (NZSC). This setting addresses limitations in traditional zero-shot coordination (ZSC), which assumes shared knowledge about the problem setting. The authors show that NZSC can be reduced to ZSC by designing a meta-Dec-POMDP with an augmented state space. They propose a simple and flexible meta-learning method called NZSC training, which trains agents across a distribution of noisy versions of coordination problems. This allows RL agents to coordinate well with novel partners even when the exact problem setting is not common knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to make artificial intelligence (AI) work better with new partners. They want AI to be able to figure out how to work together without knowing all the details about the task beforehand. The researchers create a new way of testing this called noisy zero-shot coordination. This helps AI agents learn to work well with new partners even when they don’t have all the information. |
Keywords
» Artificial intelligence » Meta learning » Reinforcement learning » Zero shot
