Summary of Symmetry-breaking Augmentations For Ad Hoc Teamwork, by Ravi Hammond et al.
Symmetry-Breaking Augmentations for Ad Hoc Teamwork
by Ravi Hammond, Dustin Craggs, Mingyu Guo, Jakob Foerster, Ian Reid
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a solution to the challenge of adapting artificial intelligence (AI) agents to new teammates with unknown or unobserved strategies. The authors introduce symmetry-breaking augmentations (SBA), which increases diversity in the behavior of training teammates by applying a symmetry-flipping operation. This approach helps AI agents learn to respond better to novel teammates, improving performance in collaborative settings. The paper demonstrates SBA experimentally in two settings and shows improved results compared to previous ad hoc teamwork approaches in the card game Hanabi. Additionally, the authors propose a general metric for estimating symmetry-dependency amongst a given set of policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI agents are having trouble working with new teammates that do something different than what they’ve seen before. This is like trying to drive on the opposite side of the road if you’re used to driving only on one side! To help AI agents adapt, researchers introduce a new way to mix up the behaviors of their training teammates. By learning how to respond to these mixed-up teammates, AI agents become better at working with new teammates and doing tasks like playing cards or following directions. |
