  Reinforcement learning algorithms are gaining popularity in fields in which
optimal scheduling is important, and oncology is not an exception. The complex
and uncertain dynamics of cancer limit the performance of traditional
model-based scheduling strategies like Optimal Control. Motivated by the recent
success of model-free Deep Reinforcement Learning (DRL) in challenging control
tasks and in the design of medical treatments, we use Deep Q-Network (DQN) and
Deep Deterministic Policy Gradient (DDPG) to design a personalized cancer
chemotherapy schedule. We show that both of them succeed in the task and
outperform the Optimal Control solution in the presence of uncertainty.
Furthermore, we show that DDPG can exterminate cancer more efficiently than DQN
presumably due to its continuous action space. Finally, we provide some insight
regarding the amount of samples required for the training.
