Deep Reinforcement Learning for Personalized Dose Optimization in Oncology Treatment

Abstract

Personalized dosing in oncology offers the promise of maximizing therapeutic benefit while minimizing toxicity, yet clinical practice remains constrained by population-level guidelines and limited individualized decision support. Deep reinforcement learning (DRL)   which combines representation learning with sequential decision-making under uncertainty   provides a principled framework for learning individualized, time-varying dose policies from longitudinal patient data and simulated environments. This article develops a comprehensive, scholarly, and application-oriented treatment of DRL for personalized dose optimization in oncology. We synthesize theoretical foundations, model architectures, environment and reward design, safety and interpretability considerations, evaluation protocols, and translational pathways toward clinical deployment. We critically review opportunities and limitations, present methodological best practices, and propose a research and validation roadmap bridging preclinical simulation, retrospective evaluation, and prospective trials. Throughout, we ground discussion in established work on reinforcement learning and clinical decision support and highlight domain-specific challenges in oncology (heterogeneous tumor biology, delayed outcomes, sparse labels, and strong safety constraints). This manuscript is intended as a near-submission-ready review + methods article for researchers developing DRL-driven precision dosing systems in cancer care.