Actor-Critic Reinforcement Learning for Personalized Diabetes Management: A Matrix-Game Approach with Simulation and Visualization

Reinforcement learning (RL) provides a flexible framework for optimizing personalized healthcare treatment options. In this work, we implemented a diabetes management problem using actor-critic reinforcement learning from a matrix game perspective, with the goal of maximizing long-term health outcomes. We simulated a diabetic patient over 20 weeks, with the actor recommending treatment plans (e.g., insulin and dietary interventions), while the critic determined the patient's benefit. The model was able to learn patient-specific plans that recommended treatment with normal blood sugar levels more frequently (75%) compared to using a fixed baseline (50%). We also developed a Python application to simulate diabetes models, providing visualizations of policy evolution, health outcomes, and value estimates. The results of this work demonstrate promising signs for integrating reinforcement learning into healthcare precision medicine, while highlighting future implementation challenges, addressing relevant safety constraints and appropriate data use.

Reinforcement learning, Actor-Critic, diabetes management, personalized medicine, matrix-game, simulation

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