{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/group-driven-reinforcement-learning-for","title":"Group-driven Reinforcement Learning for Personalized mHealth Intervention","arxiv_id":"1708.04001","date":"2017-08-14","proceeding":null,"authors":["Feiyun Zhu","Jun Guo","Zheng Xu","Peng Liao","Junzhou Huang"],"abstract":"Due to the popularity of smartphones and wearable devices nowadays, mobile\nhealth (mHealth) technologies are promising to bring positive and wide impacts\non people's health. State-of-the-art decision-making methods for mHealth rely\non some ideal assumptions. Those methods either assume that the users are\ncompletely homogenous or completely heterogeneous. However, in reality, a user\nmight be similar with some, but not all, users. In this paper, we propose a\nnovel group-driven reinforcement learning method for the mHealth. We aim to\nunderstand how to share information among similar users to better convert the\nlimited user information into sharper learned RL policies. Specifically, we\nemploy the K-means clustering method to group users based on their trajectory\ninformation similarity and learn a shared RL policy for each group. Extensive\nexperiment results have shown that our method can achieve clear gains over the\nstate-of-the-art RL methods for mHealth.","url_abs":"http://arxiv.org/abs/1708.04001v1","url_pdf":"http://arxiv.org/pdf/1708.04001v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"group-driven-reinforcement-learning-for","repo_url":"https://github.com/alielhassouni/heartsteps-gaussian-generative-model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"k-means-clustering","method_name":"k-Means Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.04001","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}