由于癌症的慢性和危险性，研究人员探索了各种方法，使用新的治疗方法来控制与这种疾病相关的异常细胞生长。本研究介绍了一种基于归一化优势函数强化学习的控制系统。它的目的是增强人体针对癌细胞增殖的免疫反应。这种控制方法首次用于提供化疗和抗血管生成药物的组合，而不需要复杂的、预定义的数学模型。它采用无模型强化学习技术，可根据患者个体进行适应性调整，以确定以最小注射速率的最佳给药方案。在这方面，采用全面且真实的模拟和训练环境，以正常细胞、癌细胞和内皮细胞的浓度以及化疗和抗血管生成剂的水平作为状态变量。此外，在模拟中考虑了高水平的干扰，以研究所提出的方法针对治疗过程或患者参数中可能的不确定性的鲁棒性。还根据医疗目标设计了实用的奖励功能，以确保有效和安全的治疗结果。与现有方法相比，结果证明了鲁棒性和卓越的性能。模拟表明，所提出的方法是一种可靠的策略，可以使用最小剂量的化疗和抗血管生成药物在最短的时间内有效降低癌细胞的浓度。版权所有 © 2024 Elsevier Ltd. 保留所有权利。

By virtue of the chronic and dangerous nature of cancer, researchers have explored various approaches to managing the abnormal cell growth associated with this disease using novel treatment methods. This study introduces a control system based on normalized advantage function reinforcement learning. It aims to boost the body's immune response against cancer cell proliferation. This control approach is applied to provide a combination of both chemotherapy and anti-angiogenic drugs for the first time without the need for complex, predefined mathematical models. It employs a model-free reinforcement learning technique that adaptively adjusts to individual patients to determine optimal drug administration with minimum injection rates. In this regard, a comprehensive and realistic simulation and training environment is employed, with the concentrations of normal cells, cancer cells, and endothelial cells, as well as the levels of chemotherapy and anti-angiogenic agents, as state variables. Furthermore, high levels of disturbances are considered in the simulation to investigate the robustness of the proposed method against probable uncertainties in the treatment process or patient parameters. A practical reward function has also been devised in alignment with medical objectives to ensure effective and safe treatment outcomes. The results demonstrate robustness and superior performance compared to the existing methods. Simulations show that the proposed approach is a dependable strategy for effectively reducing the concentration of cancer cells in the shortest duration using minimal doses of chemotherapy and anti-angiogenic drugs.Copyright © 2024 Elsevier Ltd. All rights reserved.