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
Publisher name:  Springer
Volume Information:  volume 54, pages:3215–3238 (2021)
Paper Link:   https://link.springer.com/article/10.1007/s10462-020-09938-y