A Review of Cooperation in Multi-agent Learning
Abstract
Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous disciplines, including game theory, economics, social sciences, and evolutionary biology. Research in this area aims to understand both how agents can coordinate effectively when goals are aligned and how they may cooperate in settings where gains from working together are possible but possibilities for conflict abound. In this paper we provide an overview of the fundamental concepts, problem settings and algorithms of multi-agent learning. This encompasses reinforcement learning, multi-agent sequential decision-making, challenges associated with multi-agent cooperation, and a comprehensive review of recent progress, along with an evaluation of relevant metrics. Finally we discuss open challenges in the field with the aim of inspiring new avenues for research.
Cite
@article{arxiv.2312.05162,
title = {A Review of Cooperation in Multi-agent Learning},
author = {Yali Du and Joel Z. Leibo and Usman Islam and Richard Willis and Peter Sunehag},
journal= {arXiv preprint arXiv:2312.05162},
year = {2023}
}
Comments
29 pages, 3 figures