scholarly journals Network Resource Allocation Strategy Based on Deep Reinforcement Learning

2020 ◽  
Vol 1 ◽  
pp. 86-94
Author(s):  
Shidong Zhang ◽  
Chao Wang ◽  
Junsan Zhang ◽  
Youxiang Duan ◽  
Xinhong You ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Allocation Strategy ◽  
Network Resource ◽  
Network Resource Allocation ◽  
Resource Allocation Strategy

scholarly journals Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

2021 ◽  
Vol 11 (22) ◽  
pp. 10870
Author(s):  
Abdikarim Mohamed Ibrahim ◽  
Kok-Lim Alvin Yau ◽  
Yung-Wey Chong ◽  
Celimuge Wu
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Network Routing ◽  
Network Resource ◽  
Network Resource Allocation ◽  
Significant Performance ◽  
Multi Agent ◽  
Taxonomic Approach ◽  
And Performance ◽  
Reinforcement Learning Models

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.

scholarly journals Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6542
Author(s):  
Ida Nurcahyani ◽  
Jeong Woo Lee
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Vehicular Networks ◽  
Learning Algorithms ◽  
Vehicular Network ◽  
Machine Learning Algorithms ◽  
Allocation Strategy ◽  
Network Resources ◽  
Network Resource ◽  
Resource Allocation Strategy

The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study.

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