Optimal STI controls for HIV patients based on an efficient deep Q learning method

We investigate an efficient computational tool to suggest useful treatment regimens for people infected with the human immunodeficiency virus (HIV). Structured treatment interruption (STI) is a regimen in which therapeutic drugs are periodically administered and withdrawn to give patients relief from an arduous drug therapy. Numerous studies have been conducted to find better STI treatment strategies using various computational tools with mathematical models of HIV infection. In this paper, we leverage a modified version of the double deep Q network with prioritized experience replay to improve the performance of classic deep learning algorithms. Numerical simulation results show that our methodology produces significantly more optimal cost values for shorter treatment periods compared to other recent studies. Furthermore, our proposed algorithm performs well in one-day segment scenarios, whereas previous studies only reported results for five-day segment scenarios.