Deep reinforcement learning-based control of chemo-drug dose in cancer treatment

•Proposing a deep RL-based controller for cancer chemotherapy, which handles states and action in infinite space.•The proposed approach does not need to have a mathematical model of the system, it just needs to have tumor volume.•Providing an adaptive control technique to respond to the special conditions and diagnosis measurements of different categories of patients.•Evaluating and comparing the efficiency and robustness of the proposed method using patients with diverse conditions. Advancement in the treatment of cancer, as a leading cause of death worldwide, has promoted several research activities in various related fields. The development of effective treatment regimens with optimal drug dose administration using a mathematical modeling framework has received extensive research attention during the last decades. However, most of the control techniques presented for cancer chemotherapy are mainly model-based approaches. The available model-free techniques based on Reinforcement Learning (RL), commonly discretize the problem states and variables, which other than demanding expert supervision, cannot model the real-world conditions accurately. The more recent Deep Reinforcement Learning (DRL) methods, which enable modeling the problem in its original continuous space, are rarely applied in cancer chemotherapy. In this paper, we propose an effective and robust DRL-based, model-free method for the closed-loop control of cancer chemotherapy drug dosing. A nonlinear pharmacological cancer model is used for simulating the patient and capturing the cancer dynamics. In contrast to previous work, the state variables and control action are modeled in their original infinite spaces to avoid expert-guided discretization and provide a more realistic solution. The DRL network is trained to automatically adjust the drug dose based on the monitored states of the patient. The proposed method provides an adaptive control technique to respond to the special conditions and diagnosis measurements of different categories of patients. The performance of the proposed DRL-based controller is evaluated by numerical analysis of different diverse simulated patients. Comparison to the state-of-the-art RL-based method, which uses discretized state and action spaces, shows the superiority of the approach in the process and duration of cancer chemotherapy treatment. In the majority of the studied cases, the proposed model decreases the medication period and the total amount of adminis