Spectrum allocation based on Q-Learning algorithm in femtocell networks

2012 ◽  
Author(s):  
Xiangfen Ji ◽  
Zhu Qi ◽  
Zhao Su
Keyword(s):  
Learning Algorithm ◽  
Spectrum Allocation ◽  
Femtocell Networks ◽  
Q Learning

An Adaptive Spectrum Allocation Algorithm in Femtocell Networks Using Q-learning

2013 ◽  
Vol 12 (23) ◽  
pp. 7353-7360
Author(s):  
Bi Yu-ting ◽  
Chen Xin ◽  
Wang Hong-lu ◽  
Liu Zong-Qi
Keyword(s):  
Spectrum Allocation ◽  
Femtocell Networks ◽  
Allocation Algorithm ◽  
Q Learning

Application of improved Q learning algorithm to job shop problem

2009 ◽  
Vol 28 (12) ◽  
pp. 3268-3270
Author(s):  
Chao WANG ◽  
Jing GUO ◽  
Zhen-qiang BAO
Keyword(s):  
Job Shop ◽  
Learning Algorithm ◽  
Q Learning

scholarly journals Aircraft Maintenance Check Scheduling Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 113
Author(s):  
Pedro Andrade ◽  
Catarina Silva ◽  
Bernardete Ribeiro ◽  
Bruno F. Santos
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
Q Learning ◽  
Scheduling Policy ◽  
Real Scenario ◽  
Maintenance Plan ◽  
Small Disturbances

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.

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