Owing to early diagnosis and treatment of cancer as a prerequisite in recent times, the role of machine learning has been increased substantially. The mathematically powerful and optimized solutions for the detection and cure of cancer are constantly being explored and novel models based upon standard algorithms are also being developed. Leveraging one such solution is Reinforcement Learning (RL), which is a semi-supervised type of learning. The paper presents a detailed discussion on the various RL techniques, algorithms, and open issues, in addition to the review of literature for diagnosis and treatment of cancer. A smaller number of publications for diagnosis and treatment of cancer have been reported before 2011 but now after the success of Deep Learning (DL) and the advent of Deep Reinforcement Learning (DRL), the publications have grown in number from 2017 onwards. The scope of RL for cancer diagnosis and treatment is also demystified and provides the research community with the insights of how to formulate RL problem as a Cancer diagnostic problem. RL has been found successful for landmark detection in medical images and optimal control of drugs and radiations.

近年来，由于癌症的早期诊断和治疗成为先决条件，机器学习的作用已大大增加。人们不断探索用于检测和治疗癌症的强大且优化的数学解决方案，并且还开发了基于标准算法的新颖模型。利用这样的解决方案之一是强化学习（RL），它是一种半监督学习类型。除了对癌症诊断和治疗的文献综述之外，本文还详细讨论了各种 RL 技术、算法和开放问题。2011 年之前，有关癌症诊断和治疗的出版物数量较少，但随着深度学习 (DL) 的成功和深度强化学习 (DRL) 的出现，从 2017 年开始，出版物的数量不断增加。强化学习在癌症诊断和治疗中的范围也被揭开神秘面纱，并为研究界提供了如何将强化学习问题表述为癌症诊断问题的见解。强化学习已成功用于医学图像中的地标检测以及药物和辐射的最佳控制。