# Learning to Cooperate with Completely Unknown Teammates

arXiv: https://arxiv.org/abs/2205.03289 · HTML: https://mlsanity.com/paper/2205.03289/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2022.

## Built on by (most cited first, 1 of 1)

- [RecBayes: Recurrent Bayesian Ad Hoc Teamwork in Large Partially Observable Domains](https://mlsanity.com/paper/2506.15756/) (2025)

## Abstract (arXiv)

A key goal of ad hoc teamwork is to develop a learning agent that cooperates with unknown teams, without resorting to any pre-coordination protocol. Despite a vast number of ad hoc teamwork algorithms in the literature, most of them cannot address the problem of learning to cooperate with a completely unknown team, unless it learns from scratch. This article presents a novel approach that uses transfer learning alongside the state-of-the-art PLASTIC-Policy to adapt to completely unknown teammates quickly. We test our solution within the Half Field Offense simulator with five different teammates. The teammates were designed independently by developers from different countries and at different times. Our empirical evaluation shows that it is advantageous for an ad hoc agent to leverage its past knowledge when adapting to a new team instead of learning how to cooperate with it from scratch.