Knowledge Commons of Institute of Automation,CAS
Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox | |
Qiyue Yin1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | Machine Intelligence Research
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ISSN | 2731-538X |
2024 | |
卷号 | 21期号:3页码:411-430 |
摘要 | 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. |
关键词 | Deep reinforcement learning, distributed machine learning, self-play, population-play, toolbox |
DOI | 10.1007/s11633-023-1454-4 |
七大方向——子方向分类 | 其他 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
中文导读 | https://mp.weixin.qq.com/s/5Cd-0Z5teDsJw6-Rz8sG_g |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56474 |
专题 | 学术期刊_Machine Intelligence Research |
作者单位 | 1.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 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 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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MIR-2022-12-400.pdf(2923KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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