# Few-Shot Teamwork

arXiv: https://arxiv.org/abs/2207.09300 · HTML: https://mlsanity.com/paper/2207.09300/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 19 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design](https://mlsanity.com/paper/2012.02096/) (2020)
- [Learning to Teach in Cooperative Multiagent Reinforcement Learning](https://mlsanity.com/paper/1805.07830/) (2018)
- [Reasoning about Hypothetical Agent Behaviours and their Parameters](https://mlsanity.com/paper/1906.11064/) (2019)
- [Belief and Truth in Hypothesised Behaviours](https://mlsanity.com/paper/1507.07688/) (2015)

## Built on by (most cited first, 2 of 2)

- [Deep Reinforcement Learning for Multi-Agent Interaction](https://mlsanity.com/paper/2208.01769/) (2022)
- [Making Friends in the Dark: Ad Hoc Teamwork Under Partial Observability](https://mlsanity.com/paper/2310.01439/) (2023)

## Abstract (arXiv)

We propose the novel few-shot teamwork (FST) problem, where skilled agents trained in a team to complete one task are combined with skilled agents from different tasks, and together must learn to adapt to an unseen but related task. We discuss how the FST problem can be seen as addressing two separate problems: one of reducing the experience required to train a team of agents to complete a complex task; and one of collaborating with unfamiliar teammates to complete a new task. Progress towards solving FST could lead to progress in both multi-agent reinforcement learning and ad hoc teamwork.