Continuous Q-Learning Resource Allocation Network

1998 ◽  
pp. 455-460 ◽  
Author(s):  
W. Ilg ◽  
K.-U. Scholl
Keyword(s):  
Resource Allocation ◽  
Learning Resource ◽  
Q Learning

scholarly journals A Q-learning based Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems Considering QoS

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Qi Zhai ◽  
Miodrag Bolic ◽  
Yong Li ◽  
Wei Cheng ◽  
Chenxi Liu
Keyword(s):  
Resource Allocation ◽  
Multiple Access ◽  
Q Learning

scholarly journals Design of English Multimedia Teaching System Based on Diversification Theory

2017 ◽  
Vol 12 (01) ◽  
pp. 119 ◽  
Author(s):  
Minsheng Lou
Keyword(s):  
Resource Allocation ◽  
Teaching Method ◽  
Multimedia Technology ◽  
Traditional Teaching ◽  
System Framework ◽  
Learning Resource ◽  
Teaching System ◽  
Teaching Activities ◽  
English Teaching ◽  
Multimedia Teaching

Based on the diversification theory, this paper designed a multimedia technology-based English teaching system framework which is used to assist English teaching in classroom. The whole system framework consists of two parts: the part of learning resource allocation and the part of English teaching activities. Learning resource allocation is mainly to expand English learning materials from the traditional printed resources to audios, videos, animations and other forms of resources; the use of multimedia resources for teaching activities is achieved mainly through the English speech teaching and lecturing device designed with the function of playing multimedia files. In order to test the application effect of multimedia technology in English teaching, this paper chose the public course of “Vocational English” as the experimental content, and compared the effect of respectively using multimedia technology to teach freshmen of Grade 2016 and adopting the traditional teaching method to teach students of Grade 2015. The outcome shows that the use of multimedia technology in English teaching can improve students’ interest in learning, reduce their pressure of learning English, and enhance their sense of accomplishment in learning, indicating that multimedia-assisted teaching can effectively improve English teaching results.

scholarly journals A Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Huashuai Zhang ◽  
Tingmei Wang ◽  
Haiwei Shen
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Data Traffic ◽  
Wireless Data ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Q Learning ◽  
Dense Networks ◽  
Target Network ◽  
Wireless Resource Allocation

The resource optimization of ultra-dense networks (UDNs) is critical to meet the huge demand of users for wireless data traffic. But the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. This paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (DRL), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep Q-learning process. To effectively allocate resources in UDNs, the DRL algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep Q-network (DQN), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the Q value. Simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. Thus, it is very meaningful to introduce the DRL to the research of UDN resource allocation.

scholarly journals Research on the Revolution of Multidimensional Learning Space in the Big Data Environment

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Weihua Huang
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Performance Optimization ◽  
Resource Sharing ◽  
Large Scale ◽  
Cluster Computing ◽  
Multidimensional Analysis ◽  
Experimental Simulation ◽  
Learning Resources ◽  
Learning Resource

Multiuser fair sharing of clusters is a classic problem in cluster construction. However, the cluster computing system for hybrid big data applications has the characteristics of heterogeneous requirements, which makes more and more cluster resource managers support fine-grained multidimensional learning resource management. In this context, it is oriented to multiusers of multidimensional learning resources. Shared clusters have become a new topic. A single consideration of a fair-shared cluster will result in a huge waste of resources in the context of discrete and dynamic resource allocation. Fairness and efficiency of cluster resource sharing for multidimensional learning resources are equally important. This paper studies big data processing technology and representative systems and analyzes multidimensional analysis and performance optimization technology. This article discusses the importance of discrete multidimensional learning resource allocation optimization in dynamic scenarios. At the same time, in view of the fact that most of the resources of the big data application cluster system are supplied to large jobs that account for a small proportion of job submissions, while the small jobs that account for a large proportion only use the characteristics of a small part of the system’s resources, the expected residual multidimensionality of large-scale work is proposed. The server with the least learning resources is allocated first, and only fair strategies are considered for small assignments. The topic index is distributed and stored on the system to realize the parallel processing of search to improve the efficiency of search processing. The effectiveness of RDIBT is verified through experimental simulation. The results show that RDIBT has higher performance than LSII index technology in index creation speed and search response speed. In addition, RDIBT can also ensure the scalability of the index system.

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