R-Learning and Gaussian Process Regression Algorithm for Cloud Job Access Control

Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Recently reinforcement learning has been given abroad attention, but when it is app...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) s. 163 - 166
Hlavní autori: Zhiping Peng, Delong Cui, Yuanjia Ma, Jianbin Xiong, Bo Xu, Weiwei Lin
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.06.2016
Predmet:
Access control
Approximation algorithms
Cloud computing
Computational modeling
Convergence
Gaussian process regression
Gaussian processes
Ground penetrating radar
R-learning
reinforcement learning
value function approximation
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Recently reinforcement learning has been given abroad attention, but when it is applied to solve problems with large-scale discrete or contiguous state space environments, the results are likely to be unsatisfactory and even fail to find optimal policies. In order to solve this problem, we establish a new generative model about the value function and use Gaussian Process Regression to approximate the state-action pairs which were never or seldom visited. We testify to the performance of the proposed algorithm by an access-control queuing job in a cloud computing environment. The computational results demonstrate the scheme can balance the exploration and exploitation in the learning process and accelerate the convergence to a certain extent.
DOI:10.1109/CSCloud.2016.15