Hepatitis B Virus Infection Control Using Reinforcement Learning

In this paper, optimal drug schedule for patients infected by hepatitis B virus (HBV) is obtained. An objective of the control is reducing infected cells and free virions. The optimal control problem is to design an effective drug-schedule to reduce the number of infected cells and free virions in a time-optimal fashion. To achieve this goal, a reinforcement learning (RL), which is one of the best unsupervised machine learning algorithms, is proposed for control. Because RL has no need of environment model, i.e. it is model-free; it has absorbed interests during the recent years, especially in medical applications. Performance evaluation of the proposed algorithm has been performed by simulating on the mathematical model of drug dosage of hepatitis therapy.

کلیدواژه ها:

drug therapy ، hepatitis B virus infection ، optimal control ، reinforcement learning