A Review of Cooperation in Multi-agent Learning

2023/12/08 by Yali Du, Joel Z. Leibo, Du, Yali +7 · 10 citations
Decision Sciences · Social Sciences · #Artificial Intelligence (cs.AI) #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #Evolutionary Game Theory and Cooperation #FOS: Computer and information sciences #Game Theory and Applications #Machine Learning (cs.LG) #Multiagent Systems (cs.MA)

paper · pdf · doi:10.48550/arxiv.2312.05162

openalex publication_date 2023/12/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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