As a learning method, Reinforcement Learning interacts with the environment in order to find the optimal policy with the maximal expected cumulative reward. But when it is applied to solve problems with large-scale discrete or contiguous state space environments such as resource allocation, job schedule, and access control etc., 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.
['Zhiping Peng', 'Delong Cui', 'Jianbin Xiong', 'Bo Xu', 'Yuanjia Ma', 'Weiwei Lin']
Cloud Job Access Control Scheme Based on Gaussian Process Regression and Reinforcement Learning