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Chemotherapy Schedules via Reinforcement Learning by 1165090759 is a document available to read on EtoBox.

This study explores the use of reinforcement learning to develop robust chemotherapeutic dosing schedules for cancer treatment, demonstrating that these schedules are more resilient to patient-specific parameter variations compared to traditional optimal control methods. By training a reinforcement learning agent on mean-value parameters and utilizing measurable metrics like bone marrow density, the researchers achieved improved dosing strategies that minimize drug toxicity. The findings suggest that reinfo

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1165090759

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EN