Online learning based on a novel cost function for system power management

A novel system power management technique is proposed that employs a novel cost function based on state-action. Compared with the conventional algorithm, by using multiple parameter constraints in cost function of power management framework, the improved Q-learning can effectively make decisions to achieve a rational optimisation room. The proposed power management framework does not need any prior data and is running on a power model. As uncertainties can be effectively captured and modelled, the framework based on the model can help to explore an ideal trade-off and converge to the best power management policy. The results obtained showed that improved algorithm achieved remarkable significance.