The main purpose of this paper is to provide a solution, through which one can efficiently reduce the population of cancer cells by injecting the lowest dose of the drug; therefore, reducing the side effects of the drug on healthy cells. In this paper, a mathematical model of stem Chronic Myelogenous Leukemia (CML) is used. To this aim, a hybrid method is used, that is a combination of the Eligibility Traces algorithm and Neural Networks. The eligibility traces algorithm is one of the well-known methods for solving problems under the Reinforcement Learning (RL) approach. The reason is that the population of cancer cells can be controlled with a higher accuracy and will have a significant impact on dosage of injection. The eligibility traces algorithm has the advantage of backward view, meaning it will investigate previous states, as well. That will result in improving the learning procedure, speed of reduction in cancer cells population and the total dosage of the injected drug during the treatment period, in patients with CML. Combination of the mentioned method and neural networks has provided continuous states in the considered problem. Hence, there will be no limitation for considering all possible states for solving the problem. Moreover, this can accelerate obtaining the optimal dosage with a high accuracy, which is a significant advantage of the proposed method. To show the effectiveness of the proposed method to control the population of cancer cells and obtaining the optimal dosage, it is compared with four different cases: when only the eligibility traces algorithm is employed, in the case only the Q-learning algorithm is used, when the Optimal Control is applied and in the case no dosage is injected. Finally, it is revealed that the combinatory method of the eligibility traces algorithm and neural networks can control the population of cancer cells more quickly, with a higher accuracy as well as applying a lower dosage of the drug.

本文的主要目的是提供一种解决方案，通过该解决方案，可以通过注射最低剂量的药物有效减少癌细胞的数量。因此，减少了药物对健康细胞的副作用。在本文中，使用了干性慢性粒细胞白血病（CML）的数学模型。为此，使用了一种混合方法，该方法是“资格跟踪”算法和神经网络的组合。资格跟踪算法是在强化学习（RL）方法下解决问题的一种众所周知的方法。原因是癌细胞的数量可以得到更高的精确度控制，并且将对注射剂量产生重大影响。资格跟踪算法具有后向视图的优势，这意味着它也将调查以前的状态。这将改善CML患者在治疗期间的学习过程，癌细胞数量减少的速度以及所注射药物的总剂量。所提到的方法和神经网络的结合在所考虑的问题中提供了连续状态。因此，考虑解决问题的所有可能状态将没有限制。而且，这可以加速以高精度获得最佳剂量，这是所提出的方法的显着优点。为了显示所提出的控制癌细胞数量并获得最佳剂量的方法的有效性，将其与四种不同情况进行了比较：当仅使用资格跟踪算法时，在仅使用Q学习算法的情况下，当应用最佳控制并且在没有注射剂量的情况下。最后，揭示了资格跟踪算法和神经网络的组合方法可以更快地控制癌细胞的数量，具有更高的准确度以及使用更低剂量的药物。