基于强化学习的癌症化疗治疗方案控制研究
Reinforcement Learning based control of Cancer Chemotherapy treatments
AI 导读
台北大学的一篇硕士论文研究了用强化学习生成癌症化疗的序贯治疗最优策略，先用传统 Q-learning 进行化疗仿真，再扩展到离线强化学习。作者提出的 Chemo RL 模型基于真实人类结肠癌临床数据，采用保守 Q-learning 与 model-based offline policy optimization 算法的 model-based 架构。
正文 · 原文
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.
| Comments: | Masters thesis at the National Taipei University. Received the IEEE Taipei Section, Best Masters thesis award in 2021 |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.12940 [cs.AI] |
| (or arXiv:2611.12940v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.12940 arXiv-issued DOI via DataCite |
Submission history
From: Chamani Shiranthika Jayakody Kankanamalage [view email]
[v1]
Fri, 6 Nov 2026 16:31:55 UTC (7,729 KB)
来源：arXiv cs.AI · arxiv.org