Paper Info
Title
Multi-agent deep reinforcement learning: a survey
Abstract
The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart during this progress, multi-agent reinforcement learning gains rapid traction, and the latest accomplishments address problems with real-world complexity. This article provides an overview of the current developments in the field of multi-agent deep reinforcement learning. We focus primarily on literature from recent years that combines deep reinforcement learning methods with a multi-agent scenario. To survey the works that constitute the contemporary landscape, the main contents are divided into three parts. First, we analyze the structure of training schemes that are applied to train multiple agents. Second, we consider the emergent patterns of agent behavior in cooperative, competitive and mixed scenarios. Third, we systematically enumerate challenges that exclusively arise in the multi-agent domain and review methods that are leveraged to cope with these challenges. To conclude this survey, we discuss advances, identify trends, and outline possible directions for future work in this research area.
Year
DOI
Venue
2022
10.1007/s10462-021-09996-w
ARTIFICIAL INTELLIGENCE REVIEW
Keywords
DocType
Volume
Multi-agent systems, Multi-agent learning, Machine learning, Reinforcement learning, Deep learning, Survey
Journal
55
Issue
ISSN
Citations 
2
0269-2821
7
PageRank 
References 
Authors
0.75
0
2
Name
Order
Citations
PageRank
Sven Gronauer171.09
Klaus Diepold243756.47