mlsanityData to 7 Oct 2026

A New Formalism, Method and Open Issues for Zero-Shot Coordination

arXiv 2106.06613 ↗First posted 11 Jun 2021cs.AI cs.LGcited by 31

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Abstract (arXiv)

In many coordination problems, independently reasoning humans are able to discover mutually compatible policies. In contrast, independently trained self-play policies are often mutually incompatible. Zero-shot coordination (ZSC) has recently been proposed as a new frontier in multi-agent reinforcement learning to address this fundamental issue. Prior work approaches the ZSC problem by assuming players can agree on a shared learning algorithm but not on labels for actions and observations, and proposes other-play as an optimal solution. However, until now, this "label-free" problem has only been informally defined. We formalize this setting as the label-free coordination (LFC) problem by defining the label-free coordination game. We show that other-play is not an optimal solution to the LFC problem as it fails to consistently break ties between incompatible maximizers of the other-play objective. We introduce an extension of the algorithm, other-play with tie-breaking, and prove that it is optimal in the LFC problem and an equilibrium in the LFC game. Since arbitrary tie-breaking is precisely what the ZSC setting aims to prevent, we conclude that the LFC problem does not reflect the aims of ZSC. To address this, we introduce an alternative informal operationalization of ZSC as a starting point for future work.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 10 of its 50 references that are in the map. First posted on arXiv 11 Jun 2021.

Builds on

10 of 50 references.

References · 10

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  3. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  4. 2019The StarCraft Multi-Agent Challengecited by 589
  5. 2019On the Utility of Learning about Humans for Human-AI Coordinationcited by 345
  6. 2020"Other-Play" for Zero-Shot Coordinationcited by 194
  7. 2017Equivalence Between Policy Gradients and Soft Q-Learningcited by 172
  8. 2018Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learningcited by 67
  9. 2020Adversarially Guided Self-Play for Adopting Social Conventionscited by 10
  10. 2018Policy Gradient With Value Function Approximation For Collective Multiagent Planningcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 6; 2024: 5; 2025: 8; 2026: 6.

31 papers.

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  1. 2023Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biasedcited by 37
  2. 2022The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Modelscited by 31
  3. 2023PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordinationcited by 25
  4. 2022Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humanscited by 23
  5. 2023Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabicited by 10
  6. 2025AssistanceZero: Scalably Solving Assistance Gamescited by 10
  7. 2023Cooperative AI via Decentralized Commitment Devicescited by 7
  8. 2022Assisting Unknown Teammates in Unknown Tasks: Ad Hoc Teamwork under Partial Observabilitycited by 6
  9. 2022For Learning in Symmetric Teams, Local Optima are Global Nash Equilibriacited by 6
  10. 2025Ad-Hoc Human-AI Coordination Challengecited by 6
  11. 2023Who Needs to Know? Minimal Knowledge for Optimal Coordinationcited by 5
  12. 2024Observation Interference in Partially Observable Assistance Gamescited by 4
19 more
  1. 2025Computing Game Symmetries and Equilibria That Respect Themcited by 3
  2. 2025The Yokai Learning Environment: Tracking Beliefs Over Space and Timecited by 3
  3. 2024Symmetry-Breaking Augmentations for Ad Hoc Teamworkcited by 2
  4. 2025Robust and Diverse Multi-Agent Learning via Rational Policy Gradientcited by 2
  5. 2025High entropy leads to symmetry-equivariant policies in Dec-POMDPscited by 2
  6. 2026Training Generalizable Collaborative Agents via Strategic Risk Aversioncited by 2
  7. 2026Shaping Zero-Shot Coordination via State Blockingcited by 2
  8. 2026Benchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamworkcited by 2
  9. 2022Ad Hoc Teamwork in the Presence of Adversariescited by 1
  10. 2023Making Friends in the Dark: Ad Hoc Teamwork Under Partial Observabilitycited by 1
  11. 2026Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Detailscited by 1
  12. 2021Multi-lingual agents through multi-headed neural networkscited by 0
  13. 2024The Benefits of Power Regularization in Cooperative Reinforcement Learningcited by 0
  14. 2024On the Complexity of Learning to Cooperate with Populations of Socially Rational Agentscited by 0
  15. 2024Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination Gamescited by 0
  16. 2025CooT: Learning to Coordinate In-Context with Coordination Transformerscited by 0
  17. 2025Efficient Reinforcement Learning for Zero-Shot Coordination in Evolving Gamescited by 0
  18. 2026Drawing with Strangers: Population Scaling Drives Zero-Shot Mutual Intelligibility in Emergent Sketchingcited by 0
  19. 2026Testing Interchangeability in LLM Agent Teamscited by 0