Using continuous reinforcement learning to obtain optimal dose of the drug in patients with melanoma during initial...

The most important issue, which is met in this paper is quick treatment of melanoma. Medically, melanoma is known as one of the most malignant types of cancers. This disease can put the patients in the risk of death, if no quick action is taken. Mostly, medical experts tolerate serious challenges to determine the optimal dose. Intelligent methods can pave this way and efficiently assist them to reliably provide the best suitable dose for quick treatment. The RL approach seems to be one of the best candidates. But, the conventional RL lacks of high accuracy and speed, due to discrete states and actions and may result in increased control effort. These drawbacks have directed us to adopt the continuous RL, a combination of NNs and the RL approach. This has increased the accuracy and optimality of the dose in a continuous state space to control and annihilate the population of cancer cells, while the complexity of the approach is significantly low. According to physicians, treatment of melanoma in its initial stage takes two months. After this period, cancer cells will be completely eliminated in the patient's body. Accordingly, a mathematical model of a patient with melanoma in initial stage is employed. The proposed method is analyzed using the Eligibility Traces algorithm, Q-learning algorithm and constant-dose injection method. The simulation results have indicated that when the combination of RL approach and NNs is adopted, after 50 days, the cancer cells will completely vanish. Besides, other parameters of the considered model will be within their normal range. However, when the Eligibility Traces and Q-learning algorithm is employed, after 50 days, cancer cells will be still present in the patient's body. When the proposed hybrid method is used, the injected dose is significantly lower than that of other methods. As a consequence, the side effects of the drug will be reduced. Finally, in this result, the effectiveness of the proposed approach is evaluated in 5 melanoma patients, under the presence of uncertainty and noise. The obtained results have confirmed the promising capability of the adopted approach to control the population of cancer cells and reach a desired level.