Who Needs to Know? Minimal Knowledge for Optimal Coordination
Niklas Lauffer, Ameesh Shah, Micah Carroll, Michael D. Dennis, Stuart Russell
Abstract
To optimally coordinate with others in cooperative games, it is often crucial to have information about one's collaborators: successful driving requires understanding which side of the road to drive on. However, not every feature of collaborators is strategically relevant: the fine-grained acceleration of drivers may be ignored while maintaining optimal coordination. We show that there is a well-defined dichotomy between strategically relevant and irrelevant information. Moreover, we show that, in dynamic games, this dichotomy has a compact representation that can be efficiently computed via a Bellman backup operator. We apply this algorithm to analyze the strategically relevant information for tasks in both a standard and a partially observable version of the Overcooked environment. Theoretical and empirical results show that our algorithms are significantly more efficient than baselines. Videos are available at https://minknowledge.github.io .
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Cited by top-tier papers2
- Robust and Diverse Multi-Agent Learning via Rational Policy GradientNiklas Lauffer, Ameesh Shah, Micah Carroll, Sanjit A. Seshia et al.NeurIPS 2025 · 4 citations
- CooT: Learning to Coordinate In-Context with Coordination TransformersHuai-Chih Wang, Hsiang-Chun Chuang, Hsi-Chun Cheng, Dai-Jie Wu et al.ICML 2026
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- Learning Agent Communication under Limited Bandwidth by Message PruningHangyu Mao, Zhengchao Zhang, Zhen Xiao, Zhibo Gong et al.AAAI 2020 · 110 citations
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