mlsanityData to 14 Oct 2026

Reinforcement Learning on Dyads to Enhance Medication Adherence

arXiv 2502.06835 ↗First posted 6 Feb 2025cs.LGcited by 2

On the map: Deep reinforcement learning methods → RL for clinical treatment and surgery

Abstract (arXiv)

Medication adherence is critical for the recovery of adolescents and young adults (AYAs) who have undergone hematopoietic cell transplantation (HCT). However, maintaining adherence is challenging for AYAs after hospital discharge, who experience both individual (e.g. physical and emotional symptoms) and interpersonal barriers (e.g., relational difficulties with their care partner, who is often involved in medication management). To optimize the effectiveness of a three-component digital intervention targeting both members of the dyad as well as their relationship, we propose a novel Multi-Agent Reinforcement Learning (MARL) approach to personalize the delivery of interventions. By incorporating the domain knowledge, the MARL framework, where each agent is responsible for the delivery of one intervention component, allows for faster learning compared with a flattened agent. Evaluation using a dyadic simulator environment, based on real clinical data, shows a significant improvement in medication adherence (approximately 3%) compared to purely random intervention delivery. The effectiveness of this approach will be further evaluated in an upcoming trial.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for clinical treatment and surgery, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 40 references that are in the map. First posted on arXiv 6 Feb 2025.

Builds on

9 of 40 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024MiWaves Reinforcement Learning Algorithm27 Aug

Other references · 8

  1. 2017A Tutorial on Thompson Samplingcited by 433
  2. 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
  3. 2021V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RLcited by 114
  4. 2014Generalization and Exploration via Randomized Value Functionscited by 112
  5. 2022Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelinescited by 22
  6. 2022Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Carecited by 18
  7. 2024A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trialcited by 11
  8. 2023Dyadic Reinforcement Learningcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directionscited by 5
  2. 2025Generalized Linear Markov Decision Processcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Boosted Distributional Reinforcement Learning: Analysis and Healthcare Applications
  2. 2024Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework
  3. 2024Adaptive Behavioral AI: Reinforcement Learning to Enhance Pharmacy Services
  4. 2025Causal Reinforcement Learning based Agent-Patient Interaction with Clinical Domain Knowledge
  5. 2026The Evidence Ladder for Reinforcement Learning in Healthcare: From Retrospective Policies to Trusted Interventions
  6. 2026Patient-Centered Treatment Planning for Chronic Multimorbidity: A Hierarchical Reinforcement Learning Framework for Preference Modeling
  7. 2024Collaborative Adaptation for Recovery from Unforeseen Malfunctions in Discrete and Continuous MARL Domains ↗
  8. 2024Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes