A Review of Cooperation in Multi-agent Learning

Autor:	Du, Yali, Leibo, Joel Z., Islam, Usman, Willis, Richard, Sunehag, Peter
Rok vydání:	2023
Předmět:	Computer Science - Multiagent Systems Computer Science - Artificial Intelligence Computer Science - Computer Science and Game Theory Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 29 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.05162 Zobrazit plný text záznamu View this record from Arxiv