_:n8798c06f26af4e279b9770aef7c41418xb39
<http://schema.org/description>
"Research has shown that personalization of health interventions can\ncontribute to an improved effectiveness. Reinforcement learning algorithms can\nbe used to perform such tailoring using data that is collected about users.\nLearning is however very fragile for health interventions as only limited time\nis available to learn from the user before disengagement takes place, or before\nthe opportunity to intervene passes. In this paper, we present a cluster-based\nreinforcement learning approach which learns across groups of users. Such an\napproach can speed up the learning process while still giving a level of\npersonalization. The clustering algorithm uses a distance metric over traces of\nstates and rewards. We apply both online and batch learning to learn policies\nover the clusters and introduce a publicly available simulator which we have\ndeveloped to evaluate the approach. The results show batch learning clearly\noutperforms online learning. Furthermore, clustering can be beneficial provided\nthat a proper clustering is found."
<https://deepai.org/profile/mark-hoogendoorn>