Application of Reinforcement Learning for optimizing the inventory management of clinical trials

Abstract

On the one hand, clinical trials are of great importance in discovering new treatments for diseases. They teach research things that cannot be learned in the laboratory, that is, what does and does not work in humans. What’s more, they are a really helpful tool for deciding whether the side effects of a new drug or treatment are acceptable compared to the potential benefits. Since results are not known at the beginning, this process is quite uncertain and effective management of doses is required. On the other hand, Reinforcement Learning is a Machine Learning paradigm different from Supervised Learning and Unsupervised Learning. Unlike these two methods, Reinforcement Learning is used when we want a system to accomplish a task by understanding the task by itself and the system must learn based on the “trial and error” rule. In recent years, several Reinforcement Learning applications have brought great advances in different fields. However, this new paradigm has not been used for optimizing the inventory management of clinical trials, so that’s why this master thesis addresses this line of investigation. The contributions of this master thesis are: (1) The definition of the Markov Decision Pro- cess formulation for the current problem, (2) The application of Tabular Methods to solve the problem, and (3) The improvement of the used Tabular Methods by modifying their respective Q-Tables. Results show a considerable improvement after the Q-Table’s modification. However, the system does not achieve a great performance mainly due to the high dimensionality of the problem.