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

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Abstract

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.

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Acknowledgements

This work is supported by the National Natural Science Foundations of China (Nos. 61672522, 61976216, and 61379101).

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  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Wei Du & Shifei Ding

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Shifei Ding

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Du, W., Ding, S. A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications. Artif Intell Rev 54, 3215–3238 (2021). https://doi.org/10.1007/s10462-020-09938-y

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