Personalization of health interventions using cluster-based reinforcement learning

Ali el Hassouni*, Mark Hoogendoorn, Martijn van Otterlo, Eduardo Barbaro

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


Research has shown that personalization of health interventions can contribute to an improved effectiveness. Reinforcement learning algorithms can be used to perform such tailoring. In this paper, we present a cluster-based reinforcement learning approach which learns optimal policies for groups of users. Such an approach can speed up the learning process while still giving a level of personalization. We apply both online and batch learning to learn policies over the clusters and introduce a publicly available simulator which we have developed to evaluate the approach. The results show batch learning significantly outperforms online learning. Furthermore, near-optimal clustering is found which proves to be beneficial in learning significantly better policies compared to learning per user and learning across all users.

Original languageEnglish
Title of host publicationPRIMA 2018 Principles and Practice of Multi-Agent Systems
Subtitle of host publication21st International Conference, Tokyo, Japan, October 29-November 2, 2018, Proceedings
EditorsNir Oren, Yuko Sakurai, Itsuki Noda, Tran Cao Son, Tim Miller, Bastin Tony Savarimuthu
Number of pages9
ISBN (Electronic)9783030030988
ISBN (Print)9783030030971
Publication statusPublished - 2018
Event21st International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2018 - Tokyo, Japan
Duration: 29 Oct 20182 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11224 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2018


  • m-Health
  • Personalization
  • Reinforcement learning


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