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Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox
Qiyue Yin1,2; Tongtong Yu1; Shengqi Shen1; Jun Yang3; Meijing Zhao1; Wancheng Ni1,2; Kaiqi Huang1,2,4; Bin Liang3; Liang Wang1,2,4
Source PublicationMachine Intelligence Research
ISSN2731-538X
2024
Volume21Issue:3Pages:411-430
Abstract

With the breakthrough of AlphaGo, deep reinforcement learning has become a recognized technique for solving sequential decision-making problems. Despite its reputation, data inefficiency caused by its trial and error learning mechanism makes deep reinforcement learning difficult to apply in a wide range of areas. Many methods have been developed for sample efficient deep reinforcement learning, such as environment modelling, experience transfer, and distributed modifications, among which distributed deep reinforcement learning has shown its potential in various applications, such as human-computer gaming and intelligent transportation. In this paper, we conclude the state of this exciting field, by comparing the classical distributed deep reinforcement learning methods and studying important components to achieve efficient distributed learning, covering single player single agent distributed deep reinforcement learning to the most complex multiple players multiple agents distributed deep reinforcement learning. Furthermore, we review recently released toolboxes that help to realize distributed deep reinforcement learning without many modifications of their non-distributed versions. By analysing their strengths and weaknesses, a multi-player multi-agent distributed deep reinforcement learning toolbox is developed and released, which is further validated on Wargame, a complex environment, showing the usability of the proposed toolbox for multiple players and multiple agents distributed deep reinforcement learning under complex games. Finally, we try to point out challenges and future trends, hoping that this brief review can provide a guide or a spark for researchers who are interested in distributed deep reinforcement learning.

KeywordDeep reinforcement learning, distributed machine learning, self-play, population-play, toolbox
DOI10.1007/s11633-023-1454-4
Sub direction classification其他
planning direction of the national heavy laboratory其他
Paper associated data
Chinese guidehttps://mp.weixin.qq.com/s/5Cd-0Z5teDsJw6-Rz8sG_g
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56474
Collection学术期刊_Machine Intelligence Research
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
3.Department of Automation, Tsinghua University, Beijing 100084, China
4.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Qiyue Yin,Tongtong Yu,Shengqi Shen,et al. Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox[J]. Machine Intelligence Research,2024,21(3):411-430.
APA Qiyue Yin.,Tongtong Yu.,Shengqi Shen.,Jun Yang.,Meijing Zhao.,...&Liang Wang.(2024).Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox.Machine Intelligence Research,21(3),411-430.
MLA Qiyue Yin,et al."Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox".Machine Intelligence Research 21.3(2024):411-430.
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