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Reinforcement learning (RL) is a promising direction in adaptive and personalized type 1 diabetes (T1D) treatment. However, the reward function – a most critical component in RL – is a component that is in most cases hand designed and often overlooked. In this paper we show that different reward functions can dramatically influence the final result when using RL to treat in-silico T1D patients.

Controlling Blood Glucose For Patients With Type 1 DiabetesUsing Deep Reinforcement Learning – The Influence OfChanging The Reward Function