Simulation-based Optimization of Chemotherapeutic Drug Dosage: An Agent-based Q-learning Approach
Subject Areas : OtherHamid Sadrian 1 , Peyman vafadoost Sabzevar 2 , Ahmad Hajipour 3 , Hamidreza Rokhsati 4
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Keywords: Cancer, Control, Reinforcement Learning, Q-Learning.,
Abstract :
Cancer is indeed a growing concern worldwide for human health and existence, with its prevalence and impact on individuals and society increasing. Main objective of this article is to control and optimize drug dosage in order to prevent the uncontrollable growth of cancer cells and also restore the patient's immune cells to normal levels at the end of the training process. In such a way that the disease can be controlled in the early days of treatment. Reinforcement learning methods are widely applied in many domains nowadays and have attracted researchers' interest in conducting studies in this field. Therefore, in this article, specifically we also use the Q-learning method, one of the most famous model-free reinforcement learning methods, as well as the four-state nonlinear dynamic model called depillis, to simulate and design the proposed controller. Proposed controller's performance was evaluated in the presence of noise in three stages (training, simulation, and both stages simultaneously) as well as in the presence of uncertainty in one of the parameters of the depillis model. In state of uncertainty, a combination therapy of chemotherapy and immunotherapy has been suggested as a treatment approach.
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