Adaptive quality of service-based routing approaches: development of neuro-dynamic state-dependent reinforcement learning algorithms

2007 ◽  
Vol 20 (10) ◽  
pp. 1113-1130 ◽  
Author(s):  
Abdelhamid Mellouk ◽  
Saïd Hoceïni ◽  
Yacine Amirat
Keyword(s):  
Quality Of Service ◽  
Reinforcement Learning ◽  
Learning Algorithms ◽  
Dynamic State ◽  
State Dependent

Reinforcement Learning for Cloud Computing Digital Library

2014 ◽  
Vol 571-572 ◽  
pp. 105-108
Author(s):  
Lin Xu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Digital Library ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Current Status ◽  
Self Learning ◽  
New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

Learning algorithms for quality of service multicast routing

1998 ◽  
Vol 34 (12) ◽  
pp. 1195 ◽  
Author(s):  
J. Reeve ◽  
P. Mars ◽  
T. Hodgkinson
Keyword(s):  
Quality Of Service ◽  
Multicast Routing ◽  
Learning Algorithms

scholarly journals BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 932
Author(s):  
Kaiwen Xia ◽  
Jing Feng ◽  
Chao Yan ◽  
Chaofan Duan
Keyword(s):  
Resource Allocation ◽  
Quality Of Service ◽  
Reinforcement Learning ◽  
Resource Utilization ◽  
Communication System ◽  
Transmission Loss ◽  
Short Message ◽  
Resource Allocation Algorithm ◽  
Processing Resources

The comprehensively completed BDS-3 short-message communication system, known as the short-message satellite communication system (SMSCS), will be widely used in traditional blind communication areas in the future. However, short-message processing resources for short-message satellites are relatively scarce. To improve the resource utilization of satellite systems and ensure the service quality of the short-message terminal is adequate, it is necessary to allocate and schedule short-message satellite processing resources in a multi-satellite coverage area. In order to solve the above problems, a short-message satellite resource allocation algorithm based on deep reinforcement learning (DRL-SRA) is proposed. First of all, using the characteristics of the SMSCS, a multi-objective joint optimization satellite resource allocation model is established to reduce short-message terminal path transmission loss, and achieve satellite load balancing and an adequate quality of service. Then, the number of input data dimensions is reduced using the region division strategy and a feature extraction network. The continuous spatial state is parameterized with a deep reinforcement learning algorithm based on the deep deterministic policy gradient (DDPG) framework. The simulation results show that the proposed algorithm can reduce the transmission loss of the short-message terminal path, improve the quality of service, and increase the resource utilization efficiency of the short-message satellite system while ensuring an appropriate satellite load balance.

Sign in / Sign up

Export Citation Format

Share Document