Who Needs to Know? Minimal Knowledge for Optimal Coordination
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 19 of its 42 references that are in the map. First posted on arXiv 15 Jun 2023.
Builds on
19 of 42 references.
References · 19
- 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
- 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 567
- 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 349
- 2020"Other-Play" for Zero-Shot Coordinationcited by 198
- 2019The Hanabi Challenge: A New Frontier for AI Researchcited by 197
- 2021Collaborating with Humans without Human Datacited by 142
- 2021V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RLcited by 114
- 2019Learning Nearly Decomposable Value Functions Via Communication Minimizationcited by 86
- 2021Differentiable Quality Diversitycited by 72
- 2019Learning Efficient Multi-agent Communication: An Information Bottleneck Approachcited by 61
- 2021On the Importance of Environments in Human-Robot Coordinationcited by 37
- 2021A New Formalism, Method and Open Issues for Zero-Shot Coordinationcited by 33
7 more
- 2019Learning Agent Communication under Limited Bandwidth by Message Pruningcited by 31
- 2019Reasoning about Hypothetical Agent Behaviours and their Parameterscited by 24
- 2013Graphical Models for Game Theorycited by 21
- 2022Equivariant Networks for Zero-Shot Coordinationcited by 16
- 2018Legible Normativity for AI Alignment: The Value of Silly Rulescited by 15
- 2018Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Ratescited by 13
- 2022On-the-fly Strategy Adaptation for ad-hoc Agent Coordinationcited by 7
Built on by
Citing papers per year: 2023: 2; 2024: 1; 2025: 2.
5 papers.
- 2023JaxMARL: Multi-Agent RL Environments and Algorithms in JAXcited by 63
- 2023ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordinationcited by 24
- 2024Symmetry-Breaking Augmentations for Ad Hoc Teamworkcited by 2
- 2025Robust and Diverse Multi-Agent Learning via Rational Policy Gradientcited by 2
- 2025CooT: Learning to Coordinate In-Context with Coordination Transformerscited by 0
- 2025Robust and Diverse Multi-Agent Learning via Rational Policy Gradient12 Nov 2025
- 2025CooT: Learning to Coordinate In-Context with Coordination Transformers30 Jun 2025
- 2024Symmetry-Breaking Augmentations for Ad Hoc Teamwork15 Feb 2024
- 2023JaxMARL: Multi-Agent RL Environments and Algorithms in JAX16 Nov 2023
- 2023ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination8 Oct 2023