Sequential decisions, changing state
This project explored insulin-dosing decisions as a reinforcement learning problem. A simulator supplied the environment and data for studying glucose management policies.
Method
Used Advantage Actor-Critic (A2C) as part of the modeling approach, connecting a learning agent with the simulated environment.
Scope
An academic simulation project, not a clinically validated dosing system. The code provides a view into the modeling and experimental setup.