mlsanityData to 7 Oct 2026

Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation

arXiv 2310.14976 ↗First posted 23 Oct 2023cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation of people with a spinal cord injury (SCI). Among other factors, using RL in this domain is difficult because there are many possible treatments (i.e., large action space) and few patients (i.e., limited training data). Treatments for SCIs have natural groupings, so we propose two approaches to grouping treatments so that an RL agent can learn effectively from limited data. One relies on domain knowledge of SCI rehabilitation and the other learns similarities among treatments using an embedding technique. We then use Fitted Q Iteration to train an agent that learns optimal treatments. Through a simulation study designed to reflect the properties of SCI rehabilitation, we find that both methods can help improve the treatment decisions of physiotherapists, but the approach based on domain knowledge offers better performance. Our findings provide a "proof of concept" that RL can be used to help improve the treatment of those with an SCI and indicates that continued efforts to gather data and apply RL to this domain are worthwhile.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 154 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

20 of 154 references.

References · 20

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2019Reinforcement Learning in Healthcare: A Surveycited by 308
  3. 2018Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendationcited by 101
  4. 2017Deep Reinforcement Learning for Sepsis Treatmentcited by 71
  5. 2017Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approachcited by 68
  6. 2016Estimating Dynamic Treatment Regimes in Mobile Health Using V-learningcited by 65
  7. 2017A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Unitscited by 50
  8. 2019Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learningcited by 34
  9. 2017Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processescited by 28
  10. 2017Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patientscited by 13
  11. 2018Clustering - What Both Theoreticians and Practitioners are Doing Wrongcited by 8
  12. 2019A dual mode adaptive basal-bolus advisor based on reinforcement learningcited by 8
8 more
  1. 2018An Optimal Policy for Patient Laboratory Tests in Intensive Care Unitscited by 7
  2. 2015Optimization of anemia treatment in hemodialysis patients via reinforcement learningcited by 6
  3. 2018The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Makingcited by 6
  4. 2016A Batch, Off-Policy, Actor-Critic Algorithm for Optimizing the Average Rewardcited by 5
  5. 2018Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsiscited by 5
  6. 2014Personalized Medical Treatments Using Novel Reinforcement Learning Algorithmscited by 1
  7. 2016Bounded Optimal Exploration in MDPcited by 1
  8. 2018Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RLcited by 1

Built on by

Not cited yet.