Few-Shot Teamwork
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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.
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
5 of 11 references.
References · 5
- 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 373
- 2020Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Designcited by 275
- 2018Learning to Teach in Cooperative Multiagent Reinforcement Learningcited by 45
- 2019Reasoning about Hypothetical Agent Behaviours and their Parameterscited by 24
- 2015Belief and Truth in Hypothesised Behaviourscited by 21
Built on by
Citing papers per year: 2022: 1; 2023: 1.
2 papers.