Optimization of Just-in-Time Adaptive Interventions Using Reinforcement Learning

Date

2018-01-01

Author

Gonul, Suat
Namli, Tuncay
Baskaya, Mert
Sinaci, Ali Anil
Coşar, Ahmet
Toroslu, İsmail Hakkı

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Momentary context data is an important source for intelligent decision making towards personalization of mobile phone notifications. We propose a reinforcement learning based personalized notification delivery algorithm, reasoning over momentary context data. Beyond the state of the art, we propose new approaches for faster convergence of the algorithm and jump start of learning performance at the beginning of the learning process. We test our approach in both simulated and real settings trying to optimize the timing of the notifications. Our eventual, practical aim is to make office workers more physically active during the work time. We compare the results obtained for standard and improved algorithms in both testbeds where improved versions yield better results.

Subject Keywords

Sedentary

URI

https://hdl.handle.net/11511/56210

DOI

https://doi.org/10.1007/978-3-319-92058-0_32

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

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Citation Formats

S. Gonul, T. Namli, M. Baskaya, A. A. Sinaci, A. Coşar, and İ. H. Toroslu, “Optimization of Just-in-Time Adaptive Interventions Using Reinforcement Learning,” 2018, vol. 10868, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56210.