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A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications - 2021

A Survey On Multi-Agent Deep Reinforcement Learning: From The Perspective Of Challenges And Applications

Research Area:  Machine Learning


Deep reinforcement learning has proved to be a fruitful method in various tasks in the field of artificial intelligence during the last several years. Recent works have focused on deep reinforcement learning beyond single-agent scenarios, with more consideration of multi-agent settings. The main goal of this paper is to provide a detailed and systematic overview of multi-agent deep reinforcement learning methods in views of challenges and applications. Specifically, the preliminary knowledge is introduced first for a better understanding of this field. Then, a taxonomy of challenges is proposed and the corresponding structures and representative methods are introduced. Finally, some applications and interesting future opportunities for multi-agent deep reinforcement learning are given.


Author(s) Name:  Wei Du & Shifei Ding

Journal name:  Artificial Intelligence Review

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s10462-020-09938-y

Volume Information:  volume 54, pages:3215–3238 (2021)