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The prognosis for patients with epithelial ovarian cancer remains dismaldespite improvements in survival for other cancers. Treatment involves multiplelines of chemotherapy and becomes increasingly heterogeneous after first-linetherapy. Reinforcement learning with real-world outcomes data has the potentialto identify novel treatment strategies to improve overall survival. We design areinforcement learning environment to model epithelial ovarian cancer treatmenttrajectories and use model free reinforcement learning to investigatetherapeutic regimens for simulated patients.

Patient level simulation and reinforcement learning to discover novel strategies for treating ovarian cancer