LEARNING-BASED CONTROL OF CANCER CHEMOTHERAPY TREATMENT

Abstract

The increasing threat of cancer to human life and the improvement in survival rate of this disease due to effective treatment has promoted research in various related fields. This research has shaped clinical trials and emphasized the necessity to properly schedule cancer chemotherapy to ensure effective and safe treatment. Most of the control methodologies proposed for cancer chemotherapy scheduling treatment are model-based. In this paper, a reinforcement learning (RL)-based, model-free method is proposed for the closed-loop control of cancer chemotherapy drug dosing. Specifically, the Q-learning algorithm is used to develop an optimal controller for cancer chemotherapy drug dosing. Numerical examples are presented using simulated patients to illustrate the performance of the proposed RL-based controller.