Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 359–370 (1994)
Hoogendoorn, M., Funk, B.: Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer, New York City (2017). https://doi.org/10.1007/978-3-319-66308-1
Kranzler, H.R., McKay, J.R.: Personalized treatment of alcohol dependence. Curr. Psychiatry Rep. 14(5), 486–493 (2012)
Lagoudakis, M.G., Parr, R.: Least-squares policy iteration. J. Mach. Learn. Res. 4(Dec), 1107–1149 (2003)
Lin, L.J.: Self-improving reactive agents based on reinforcement learning, planning and teaching. Mach. Learn. 8(3–4), 293–321 (1992)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. MIT press, Cambridge (2017). in progress
Taylor, M.E., Stone, P.: Transfer learning for reinforcement learning domains: a survey. J. Mach. Learn. Res. 10(Jul), 1633–1685 (2009)
Watkins, C.J., Dayan, P.: Q-learning. Mach. learn. 8(3–4), 279–292 (1992)
Wiering, M., van Otterlo, M.: Reinforcement Learning: State of the Art. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27645-3
Zhu, F., Guo, J., Xu, Z., Liao, P., Huang, J.: Group-driven reinforcement learning for personalized mHealth intervention (2017). arXiv preprint arXiv:1708.04001
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Hassouni, A.e., Hoogendoorn, M., van Otterlo, M., Barbaro, E. (2018). Personalization of Health Interventions Using Cluster-Based Reinforcement Learning. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science(), vol 11224. Springer, Cham. https://doi.org/10.1007/978-3-030-03098-8_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-03098-8_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03097-1
Online ISBN: 978-3-030-03098-8
eBook Packages: Computer ScienceComputer Science (R0)