The paper introduces a robust reinforcement learning framework that learns adaptive treatment policies for patients, accounting for uncertainties in biomarker measurements and disease dynamics. By modeling patients as hidden contexts, the method unifies generalization and robustness through robust contextual Markov decision processes. Applied to a simulator of metastatic prostate cancer, the resulting policies outperform existing adaptive therapy methods, achieving robustness with minimal performance loss and Pareto-dominating alternatives across varied scenarios.
By Chengze Xie, Xuan Wang, Ying Ding, Hairong Wang, Yan Li
The paper presents a study on adaptive chemotherapy control using deep reinforcement learning (DRL) to address tumor heterogeneity and drug resistance. Closed‑loop DRL dosing policies—continuous (TD3) and discrete (DQN)—are trained on a high‑dimensional heterogeneous tumor model and benchmarked against a Pontryagin's Maximum Principle (PMP) open‑loop solution. Across a 100‑patient virtual cohort with ±10% parameter perturbations, TD3 achieves higher average tumor reduction, while DQN offers tighter inter‑patient dosing consistency, highlighting an efficacy‑consistency trade‑off. The work assumes full observation of tumor subpopulations, noting that clinical translation will require handling sparse, noisy measurements.
By Bereket Sitotaw Kidane, Md Samiul Haque Motayed, Shuo Wang
arXiv:2606. 10376v1 Announce Type: new Abstract: Cancer treatment is at the core a sequential decision-making problem with partial observability, latent patient heterogeneity, and explicit constraints on the budget for medical measurements.
By Deniz Sargun, H. Bugra Tulay, C. Emre Koksal
arXiv:2606. 01051v1 Announce Type: new Abstract: Dynamic medical treatment requires deciding treatment intensity and intervention timing, while patient states evolve continuously and adverse events may occur between clinical interactions.
By Xun Shen, Yuepeng Wang, Akifumi Wachi, Yongqi Zhou, Richard Weiss, Yoshihiko Fujisawa, Ken Kawano, Mehrshad Sadria, Ying Chen, Xin Liu, Sebastien Gros, Xiao Hu, Kyoung-Sook Kim, Mengmou Li, Katsuki Fujisawa, Kenji Wakabayashi
arXiv:2607. 16916v1 Announce Type: new Abstract: Bladder cancer treatment requires personalized and adaptive decision-making, particularly for recurrent disease, where treatment effectiveness changes across successive clinical episodes.
By Divyansh Chawla, Anshu Garg, Isshaan Singh
arXiv:2606. 17405v1 Announce Type: new Abstract: Clinical decision support AI systems (CDSASs) must adapt to evolving patient conditions in real-time while adhering to strict safety constraints.
By Xinyu Qin, Anil K. Sood, Ruiheng Yu, Sara Corvigno, Elaine Stur, Lu Wang
The paper introduces MedGym, a benchmark environment for dynamic medical treatment recommendation that models patient evolution in continuous time using Physics-Informed Neural Networks. It addresses gaps in existing reinforcement learning (RL) approaches by allowing evaluation of RL methods under irregular measurement intervals, personalized treatment responses, and safety considerations. MedGym enables direct comparison between discrete-time and continuous-time RL methods and supports clinically relevant metrics such as personalization and trajectory-level safety.
By Yuepeng Wang, Ken Kawano, Yoshihiko Fujisawa, Yongqi Zhou, Akifumi Wachi, Mehrshad Sadria, Lei Zhou, Richard Weiss, Katsuki Fujisawa, Ying Chen, Xin Liu, Kyoung-Sook Kim, Xiao Hu, Sebastien Gros, Xun Shen
arXiv:2609.06667v1 Announce Type: new
Abstract: Adaptive dosing requires policies that reduce tumor burden without excessive toxicity. Learned dosing policies are typically judged against historical...
By Aleksandar Dimitrov, Giacomo Spigler
arXiv:2606. 01028v1 Announce Type: new Abstract: Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements and interventions are performed at irregular intervals, and treatment effects vary substantially across individuals.
By Yuepeng Wang, Ken Kawano, Yongqi Zhou, Yoshihiko Fujisawa, Richard Weiss, Akifumi Wachi, Katsuki Fujisawa, Ying Chen, Mehrshad Sadria, Xin Liu, Kyoung-Sook Kim, Xiao Hu, Sebastien Gros, Xun Shen
Purpose The WHO's COVID-19 non-pharmaceutical interventions (e. g.
arXiv:2607. 16210v1 Announce Type: new Abstract: Reinforcement learning (RL) is increasingly applied in complex, safety-critical domains, yet the lack of rigorous behavioral guarantees for neural network-based policies remains a major barrier to deployment.
By Luca Marzari, Ezio Bartocci, Enrico Marchesini
arXiv:2603. 09344v3 Announce Type: replace Abstract: Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution shift.
By Hongqiang Lin, Zhenghui Fu, Weihao Tang, Pengfei Wang, Yiding Sun, Qixian Huang, Dongxu Zhang