Who Needs to Know? Minimal Knowledge for Optimal Coordination
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.
Cite
Text
Lauffer et al. "Who Needs to Know? Minimal Knowledge for Optimal Coordination." International Conference on Machine Learning, 2023.Markdown
[Lauffer et al. "Who Needs to Know? Minimal Knowledge for Optimal Coordination." International Conference on Machine Learning, 2023.](https://mlanthology.org/icml/2023/lauffer2023icml-needs/)BibTeX
@inproceedings{lauffer2023icml-needs,
title = {{Who Needs to Know? Minimal Knowledge for Optimal Coordination}},
author = {Lauffer, Niklas and Shah, Ameesh and Carroll, Micah and Dennis, Michael D and Russell, Stuart},
booktitle = {International Conference on Machine Learning},
year = {2023},
pages = {18599-18613},
volume = {202},
url = {https://mlanthology.org/icml/2023/lauffer2023icml-needs/}
}