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

arXiv: https://arxiv.org/abs/2403.07566 · HTML: https://mlsanity.com/paper/2403.07566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 46 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [RL for clinical treatment and surgery](https://mlsanity.com/deeprl/rl-for-clinical-treatment-and-surgery/)

## Builds on (most cited first, 17 of 17)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [A Distributional Perspective on Reinforcement Learning](https://mlsanity.com/paper/1707.06887/) (2017)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Noisy Networks for Exploration](https://mlsanity.com/paper/1706.10295/) (2017)
- [Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1705.08422/) (2017)
- [Memory-based Deep Reinforcement Learning for POMDPs](https://mlsanity.com/paper/2102.12344/) (2021)
- [Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target](https://mlsanity.com/paper/1901.07510/) (2019)
- [Deep Reinforcement Learning for Closed-Loop Blood Glucose Control](https://mlsanity.com/paper/2009.09051/) (2020)
- [Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes](https://mlsanity.com/paper/2204.03376/) (2022)
- [Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation](https://mlsanity.com/paper/2005.09059/) (2020)
- [Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks](https://mlsanity.com/paper/1904.11115/) (2019)
- [Optimization of anemia treatment in hemodialysis patients via reinforcement learning](https://mlsanity.com/paper/1509.03977/) (2015)
- [On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects](https://mlsanity.com/paper/2301.00512/) (2023)

## 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.