# Ad-Hoc Human-AI Coordination Challenge

arXiv: https://arxiv.org/abs/2506.21490 · HTML: https://mlsanity.com/paper/2506.21490/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 51 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?](https://mlsanity.com/paper/2011.09533/) (2020)
- ["Other-Play" for Zero-Shot Coordination](https://mlsanity.com/paper/2003.02979/) (2020)
- [The Hanabi Challenge: A New Frontier for AI Research](https://mlsanity.com/paper/1902.00506/) (2019)
- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [Off-Belief Learning](https://mlsanity.com/paper/2103.04000/) (2021)
- [Modeling Strong and Human-Like Gameplay with KL-Regularized Search](https://mlsanity.com/paper/2112.07544/) (2021)
- [Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning](https://mlsanity.com/paper/2210.05492/) (2022)
- [Improving Policies via Search in Cooperative Partially Observable Games](https://mlsanity.com/paper/1912.02318/) (2019)
- [A New Formalism, Method and Open Issues for Zero-Shot Coordination](https://mlsanity.com/paper/2106.06613/) (2021)
- [Human-compatible driving partners through data-regularized self-play reinforcement learning](https://mlsanity.com/paper/2403.19648/) (2024)
- [Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi](https://mlsanity.com/paper/2308.10284/) (2023)
- [Human-AI Coordination via Human-Regularized Search and Learning](https://mlsanity.com/paper/2210.05125/) (2022)

## Built on by (most cited first, 8 of 8)

- [The Yokai Learning Environment: Tracking Beliefs Over Space and Time](https://mlsanity.com/paper/2508.12480/) (2025)
- [High entropy leads to symmetry-equivariant policies in Dec-POMDPs](https://mlsanity.com/paper/2511.22581/) (2025)
- [Training Generalizable Collaborative Agents via Strategic Risk Aversion](https://mlsanity.com/paper/2602.21515/) (2026)
- [Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details](https://mlsanity.com/paper/2608.03644/) (2026)
- [The Convention Gap: Beyond Literal Content in Human-AI Cooperation](https://mlsanity.com/paper/2609.11489/) (2026)
- [JaxAHT: A JAX-Based Library for Ad Hoc Teamwork](https://mlsanity.com/paper/2609.13716/) (2026)
- [Grounded Joint-Attention Other-Play for Zero-Shot Coordination](https://mlsanity.com/paper/2610.06025/) (2026)
- [What and Where in Identifying and Quantifying Conventions in Humans and AI Agents](https://mlsanity.com/paper/2610.20205/) (2026)

## Abstract (arXiv)

Achieving seamless coordination between AI agents and humans is crucial for real-world applications, yet it remains a significant open challenge. Hanabi is a cooperative card game featuring imperfect information, constrained communication, theory of mind requirements, and coordinated action -- making it an ideal testbed for human-AI coordination. However, its use for human-AI interaction has been limited by the challenges of human evaluation. In this work, we introduce the Ad-Hoc Human-AI Coordination Challenge (AH2AC2) to overcome the constraints of costly and difficult-to-reproduce human evaluations. We develop textithuman proxy agents on a large-scale human dataset that serve as robust, cheap, and reproducible human-like evaluation partners in AH2AC2. To encourage the development of data-efficient methods, we open-source a dataset of 3,079 games, deliberately limiting the amount of available human gameplay data. We present baseline results for both two- and three- player Hanabi scenarios. To ensure fair evaluation, we host the proxy agents through a controlled evaluation system rather than releasing them publicly. The code is available at hrefhttps://github.com/FLAIROx/ah2ac2https://github.com/FLAIROx/ah2ac2.