Control the population of free viruses in nonlinear uncertain HIV system using Q-learning

This paper surveys a new method to reduce the infected cells and free virus particles (virions) via a nonlinear HIV model. Three scenarios are considered for control performance evaluation. At first, the system and initial conditions are considered known completely. In the second case, the initial conditions are taken randomly. In the third scenario, in addition to uncertainty in initial condition, an additive noise is taken into account. The optimal control method is used to design an effective drug-schedule to reduce the number of infected cells and free virions with and without uncertainty. By using the Q-learning algorithm, which is the most applicable algorithm in reinforcement learning, the drug delivery rate is obtained off-line. Since Q-learning is a model-free algorithm, it is expected that the performance of the control in the presence of uncertainty does not change significantly. Simulation results confirm that the proposed control method has a good performance and high functionality in controlling the free virions for both certain and uncertain HIV models.