Self-Organizing Network Control with a TD Learning Algorithm

We study a network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing network control problem into a series of Semi Markov Decision Processes and construct a control decision model for them based on the coupled Reinforcement Learning framework. To minimize the mean weighted flow time of the jobs through the network, we propose a Reinforcement Learning algorithm to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed Reinforcement Learning algorithm applied in the investigated self-organizing network control problem.