Few-Shot Teamwork

07/19/2022
by   Elliot Fosong, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

research
07/05/2022

Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning

Successful deployment of multi-agent reinforcement learning often requir...
research
06/18/2020

Open Ad Hoc Teamwork using Graph-based Policy Learning

Ad hoc teamwork is the challenging problem of designing an autonomous ag...
research
03/24/2023

Causality Detection for Efficient Multi-Agent Reinforcement Learning

When learning a task as a team, some agents in Multi-Agent Reinforcement...
research
02/07/2020

Mobile Wireless Network Infrastructure on Demand

In this work, we introduce Mobile Wireless Infrastructure on Demand: a f...
research
10/15/2021

MLFC: From 10 to 50 Planners in the Multi-Agent Programming Contest

In this paper, we describe the strategies used by our team, MLFC, that l...
research
09/20/2018

Ad hoc Teamwork and Moral Feedback as a Framework for Safe Agent Behavior

As technology develops, it is only a matter of time before agents will b...

Please sign up or login with your details

Forgot password? Click here to reset