# Diverse Agents for Ad-Hoc Cooperation in Hanabi

arXiv: https://arxiv.org/abs/1907.03840 · HTML: https://mlsanity.com/paper/1907.03840/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 17 of 17)

- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination](https://mlsanity.com/paper/2201.12436/) (2022)
- [Collaborative Visual Navigation](https://mlsanity.com/paper/2107.01151/) (2021)
- [Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings](https://mlsanity.com/paper/2106.09086/) (2021)
- [Referential communication in heterogeneous communities of pre-trained visual deep networks](https://mlsanity.com/paper/2302.08913/) (2023)
- [Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi](https://mlsanity.com/paper/2308.10284/) (2023)
- [On-the-fly Strategy Adaptation for ad-hoc Agent Coordination](https://mlsanity.com/paper/2203.08015/) (2022)
- [Generating Diverse and Competitive Play-Styles for Strategy Games](https://mlsanity.com/paper/2104.08641/) (2021)
- [On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games](https://mlsanity.com/paper/2206.10614/) (2022)
- [Measuring Diversity of Game Scenarios](https://mlsanity.com/paper/2404.15192/) (2024)
- [Mix-ME: Quality-Diversity for Multi-Agent Learning](https://mlsanity.com/paper/2311.01829/) (2023)
- [Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi](https://mlsanity.com/paper/2203.11656/) (2022)
- [Improving Human-AI Coordination through Online Adversarial Training and Generative Models](https://mlsanity.com/paper/2504.15457/) (2025)
- [Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic](https://mlsanity.com/paper/2103.15090/) (2021)
- [Behavioral Differences is the Key of Ad-hoc Team Cooperation in Multiplayer Games Hanabi](https://mlsanity.com/paper/2303.06775/) (2023)
- [Adapting to Teammates in a Cooperative Language Game](https://mlsanity.com/paper/2403.00823/) (2024)
- [Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation](https://mlsanity.com/paper/2506.16718/) (2025)

## Abstract (arXiv)

In complex scenarios where a model of other actors is necessary to predict and interpret their actions, it is often desirable that the model works well with a wide variety of previously unknown actors. Hanabi is a card game that brings the problem of modeling other players to the forefront, but there is no agreement on how to best generate a pool of agents to use as partners in ad-hoc cooperation evaluation. This paper proposes Quality Diversity algorithms as a promising class of algorithms to generate populations for this purpose and shows an initial implementation of an agent generator based on this idea. We also discuss what metrics can be used to compare such generators, and how the proposed generator could be leveraged to help build adaptive agents for the game.