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Adaptive Chemotherapy Control under Tumor Heterogeneity via Reinforcement Learning

arXiv 2609.12264 ↗First posted 10 Sep 2026cs.LG cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for clinical treatment and surgery

Abstract (arXiv)

Designing effective chemotherapy regimens is hindered by tumor heterogeneity and drug resistance, which complicate the deployment of patient-specific model-based optimal control across diverse populations. We develop and compare closed-loop deep reinforcement learning (DRL) dosing policies with continuous (TD3) and discrete (DQN) action spaces trained on a high-dimensional heterogeneous tumor model. The DRL policies are benchmarked against a Pontryagin's Maximum Principle (PMP)-derived open-loop benchmark. We assess generalization under parametric heterogeneity using a 100-patient virtual cohort with plus or minus 10 percent uniform perturbations in growth and drug-sensitivity parameters. Across this cohort, TD3 achieves higher average tumor reduction, while DQN yields tighter inter-patient dosing consistency, revealing a clear efficacy-consistency trade-off in this study. Our simulations assume full observation of all tumor subpopulations; translation to sparse and noisy clinical measurements will require partial-observability formulations and/or state estimation. Overall, the results show that simulation-trained DRL can learn state-dependent feedback dosing policies that complement open-loop optimal control benchmarks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 29 references that are in the map. First posted on arXiv 10 Sep 2026.

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2 of 29 references.

References · 2

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2019Reinforcement Learning in Healthcare: A Surveycited by 310

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