Chronic Myelogenous Leukaemia (CML) is a haematopoietic stem cells disease with complex dynamical behaviour. One of the effective factors in treating patients is to determine the appropriate drug dosage. A physician should test the different drug dosages through trial and error in order to find its optimal value. This procedure is normally a time‐consuming and error‐prone task that can even be harmful. The contribution of this paper is to design an intelligent control strategy, which can be used to help physicians, by finding a drug treatment regimen to minimize the number of cancer cells for a CML patient. In this paper, the eligibility traces algorithm and Q‐learning approach are adopted as sub‐optimal methods for progressively reducing the population of cancer cells. In addition, the injected dosage of the drug has improved, compared with previous methods. More importantly, the proposed method is followed by the reduction in side effects of the drug. The advantage of the backward view and the previous states investigation are applied in the Eligibility Traces algorithm. These effects increase the learning procedure and decrease the growth rate of cancer cells and total dosage of the injected drug during the treatment period of time. The proposed strategy mitigates the side effects of the drug on the normal cells.

中文翻译：

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强化学习并考虑药物的副作用，从而为慢性粒细胞性白血病患者减少癌细胞的智能控制策略

慢性粒细胞性白血病（CML）是一种具有复杂动态行为的造血干细胞疾病。治疗患者的有效因素之一是确定合适的药物剂量。医生应通过反复试验来测试不同的药物剂量，以找到其最佳价值。此过程通常是一项耗时且容易出错的任务，甚至可能有害。本文的目的是设计一种智能控制策略，通过找到一种药物治疗方案来最大程度地减少CML患者的癌细胞数量，从而可以帮助医生。在本文中，采用资格跟踪算法和Q学习方法作为逐步减少癌细胞数量的次优方法。此外，药物的注射剂量已有所改善，与以前的方法相比。更重要的是，所提出的方法之后是药物副作用的减少。向后视图和先前状态调查的优势被应用在“资格跟踪”算法中。这些作用增加了学习过程，降低了癌细胞在治疗期间的生长速度和所注射药物的总剂量。所提出的策略减轻了药物对正常细胞的副作用。这些作用增加了学习过程，降低了癌细胞在治疗期间的生长速度和所注射药物的总剂量。所提出的策略减轻了药物对正常细胞的副作用。这些作用增加了学习过程，降低了癌细胞在治疗期间的生长速度和所注射药物的总剂量。所提出的策略减轻了药物对正常细胞的副作用。