期刊
APPLIED SCIENCES-BASEL
卷 11, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/app112210870
关键词
multi-agent deep reinforcement learning; reinforcement learning; multi-agent reinforcement learning; deep Q-network; applied reinforcement learning
类别
资金
- Malaysian Ministry of Education [FRGS/1/2019/ICT03/SYUC/01/1]
- Research Creativity and Management Office, Universiti Sains Malaysia
Recent advancements in deep reinforcement learning have enabled its application in multi-agent scenarios, with MADRL allowing multiple agents to interact and learn from each other. Currently, MADRL shows significant performance improvements in various multi-agent domains.
Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating environment, and learn without the need for external critics (or teachers), thereby solving complex problems. Significant performance enhancements brought about by the use of MADRL have been reported in multi-agent domains; for instance, it has been shown to provide higher quality of service (QoS) in network resource allocation and sharing. This paper presents a survey of MADRL models that have been proposed for various kinds of multi-agent domains, in a taxonomic approach that highlights various aspects of MADRL models and applications, including objectives, characteristics, challenges, applications, and performance measures. Furthermore, we present open issues and future directions of MADRL.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据