Reinforcement Learning based control of Cancer Chemotherapy treatments

A new arXiv paper (2611.12940v1) reports that a model-based offline reinforcement learning approach combining conservative Q-learning with model-based offline policy optimization can schedule cancer chemotherapy treatments, evaluated on real human colon cancer clinical data. The study also presents a simulation of chemotherapy via traditional Q-learning and reviews state-of-the-art online reinforcement learning methods for generating optimal sequential treatment policies. The authors conclude the proposed Chemo RL model addresses chemotherapy scheduling and can serve as a supportive tool for junior oncologists making treatment decisions.

arXiv:2611.12940v1 Announce Type: new Abstract: Reinforcement Learning has achieved tremendous success in recent years. Extending its scope from the traditional complex games such as Atari, Go, Pokeman, and Chess, reinforcement learning has caught researchers' attention on modelling real-life scenarios. Personalized medicine and the dynamic treatment regimen are two such preliminary research fields; researchers are focusing their mindsets currently. Estimating the optimal dynamic treatment regimens from the observational real clinical data is being popularized among the researchers, widening its scope towards more advanced versions of reinforcement learning algorithms. This research is about a research study that focused on reinforcement learning techniques that have been approached in the context of cancer chemotherapy treatments. Primarily this thesis addresses the result of the study by means of the state-of-the-art on reinforcement learning techniques used in generating the optimal policy for sequential treatments, including the existing online reinforcement learning approaches. Thereby a simulation for cancer chemotherapy via traditional Q-learning would be presented. Secondly, the study will extend towards using offline reinforcement learning in the optimal policy generation. Thus, the proposed solution follows a data-driven approach, which works on the real human colon cancer clinical data. The novelty of the proposed solution resides in the implementation of an offline reinforcement learning approach by means of a model-based architecture using the conservative Q-learning and the model-based offline policy optimization algorithm. As per the experimental results and the evaluations, it was revealed that the proposed Chemo RL model caters well the problem of cancer chemotherapy treatment scheduling and serves as a supportive model for the junior oncologists in deciding their treatment decisions.