OPTIMAL DRUG DOSAGE CONTROL STRATEGY OF IMMUNE SYSTEMS USING REINFORCEMENT LEARNING

Abstract

The treatment of cancer and other immune-related disorders remains a major challenge in modern medicine due to the highly complex, nonlinear, and uncertain dynamics of tumorimmune interactions. Conventional chemotherapy and immunotherapy regimens often rely on fixed or standardized drug dosage schedules, which frequently result in either insufficient tumor suppression or severe toxicity to healthy tissues and the immune system itself. Such static approaches fail to adapt to individual patient responses, leading to suboptimal therapeutic outcomes and increased risk of side effects. This project presents an intelligent Reinforcement Learning (RL)-based optimal drug dosage control strategy for immune systems. The proposed framework models the tumorimmune-drug interactions as a dynamic control problem and employs a critic-only reinforcement learning architecture combined with a discounted non-quadratic performance index. This approach effectively transforms the robust tracking problem with input constraints and model uncertainties into an unconstrained optimal tracking control task. The RL agent learns to determine the optimal drug dosage in real time by observing the current state of tumor cell population, immune (effector) cell population, and drug concentration. The system aims to drive the tumor burden toward a desired low or zero level while maintaining immune cell counts within a safe and effective range, thereby achieving a superior balance between therapeutic efficacy and toxicity minimization. The framework was implemented and extensively evaluated using established mathematical models of tumor-immune dynamics under various uncertainty conditions and dosage constraints. Simulation results demonstrate that the proposed RL-based controller achieves effective tumor suppression with significantly lower cumulative drug usage compared to traditional fixed-dose strategies. It exhibits strong robustness to parameter variations and external disturbances while preserving healthy immune function. This work offers a promising foundation for developing adaptive, personalized, and intelligent drug dosing systems in precision oncology and immunotherapy.