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pH-RL: A personalization architecture to bring reinforcement learning to health practice [article]

Ali el Hassouni, Mark Hoogendoorn, Marketa Ciharova, Annet Kleiboer, Khadicha Amarti, Vesa Muhonen, Heleen Riper, A. E. Eiben
2021 arXiv   pre-print
This paper presents pH-RL (personalization in e-Health with RL) a general RL architecture for personalization to bring RL to health practice. pH-RL allows for various levels of personalization in health  ...  While reinforcement learning (RL) has proven to be the approach of choice for tackling many complex problems, it remains challenging to develop and deploy RL agents in real-life scenarios successfully.  ...  Fig. 1 : 1 pH-RL: A reinforcement learning personalization architecture for mobile applications in e-Health pH-RL: A personalization architecture to bring RL to health practice Fig. 2 : 2 Week 1 action  ... 
arXiv:2103.15908v2 fatcat:h5de275u5jexdmfbexjnvu2rba

Offline Policy Comparison under Limited Historical Agent-Environment Interactions [article]

Anton Dereventsov and Joseph D. Daws Jr. and Clayton Webster
2021 arXiv   pre-print
We address the challenge of policy evaluation in real-world applications of reinforcement learning systems where the available historical data is limited due to ethical, practical, or security considerations  ...  In addition we present the Limited Data Estimator (LDE) as a simple method for evaluating and comparing policies from a small number of interactions with the environment.  ...  Eiben. ph-rl: A personalization architecture to bring reinforcement learning to health practice. arXiv preprint arXiv:2103.15908, 2021. M. Hauskrecht and H. Fraser.  ... 
arXiv:2106.03934v1 fatcat:7qdbptejevcv5cbh4rwawgrfre