mlsanityData to 7 Oct 2026

An Improved Strategy for Blood Glucose Control Using Multi-Step Deep Reinforcement Learning

arXiv 2403.07566 ↗First posted 12 Mar 2024cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Blood Glucose (BG) control involves keeping an individual's BG within a healthy range through extracorporeal insulin injections is an important task for people with type 1 diabetes. However,traditional patient self-management is cumbersome and risky. Recent research has been devoted to exploring individualized and automated BG control approaches, among which Deep Reinforcement Learning (DRL) shows potential as an emerging approach. In this paper, we use an exponential decay model of drug concentration to convert the formalization of the BG control problem, which takes into account the delay and prolongedness of drug effects, from a PAE-POMDP (Prolonged Action Effect-Partially Observable Markov Decision Process) to a MDP, and we propose a novel multi-step DRL-based algorithm to solve the problem. The Prioritized Experience Replay (PER) sampling method is also used in it. Compared to single-step bootstrapped updates, multi-step learning is more efficient and reduces the influence from biasing targets. Our proposed method converges faster and achieves higher cumulative rewards compared to the benchmark in the same training environment, and improves the time-in-range (TIR), the percentage of time the patient's BG is within the target range, in the evaluation phase. Our work validates the effectiveness of multi-step reinforcement learning in BG control, which may help to explore the optimal glycemic control measure and improve the survival of diabetic patients.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 46 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

17 of 46 references.

References · 17

  1. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  2. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  3. 2015Prioritized Experience Replaycited by 1,070
  4. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 774
  5. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  6. 2017A Distributional Perspective on Reinforcement Learningcited by 743
  7. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  8. 2017Noisy Networks for Explorationcited by 262
  9. 2017Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approachcited by 68
  10. 2021Memory-based Deep Reinforcement Learning for POMDPscited by 53
  11. 2019Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Targetcited by 31
  12. 2020Deep Reinforcement Learning for Closed-Loop Blood Glucose Controlcited by 26
5 more
  1. 2022Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetescited by 22
  2. 2020Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validationcited by 18
  3. 2019Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networkscited by 8
  4. 2015Optimization of anemia treatment in hemodialysis patients via reinforcement learningcited by 6
  5. 2023On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effectscited by 4

Built on by

Not cited yet.