method to learn the network by using the warm start trajectory, which directly reflects the users’ property. The optimization of our model is difficult and very different from the general supervised learning due to the indirect observation of values. As a contribution, we propose two algorithms for the proposed online RLs. Apart from mHealth, the proposed methods can be easily applied or adapted to other health-related tasks. Extensive experiment results on the HeartSteps dataset demonstrates that in a variety of parameter settings, the proposed two methods obtain obvious improvements over the state-of-the-art methods.'
author:
- 'Feiyun Zhu$^{\star,\ddagger}$, Peng Liao$^{\ddagger}$, Xinliang Zhu$^{\star}$, Yaowen Yao$^{\star}$, Junzhou Huang$^{\star}$ [^1]'
bibliography:
- '4\_home\_fyzhu\_link2dropbox\_self\_Folder\_myWorksOnDropboxs\_bibFiles\_referenceBib2.bib'
- '5\_home\_fyzhu\_link2dropbox\_self\_Folder\_myWorksOnDropboxs\_bibFiles\_referenceBib.bib'
title: 'Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention'
---

@path[[/home/fyzhu/link2dropbox/self\_Folder/myWorksOnDropboxs/201702\_SigKDD\_CohesionDiscovery4onlineGraphRL\_self/IEEE\_TPAMI//]{}]{}

Actor-Critic, Reinforcement Learning, Mobile Health (mHealth) Intervention, Cohesion

Introduction
============

With billions of smart device[^2] users globally, it is increasingly popular among the scientist community to make use of the state-of-the-art articial intelligence and mobile health technologies to leverage supercomputers and big data to facilicate the prediction of healthcare tasks [@huitian_2014_NIPS_ActCriticBandit4JITAI; @huitian_2016_PhdThesis_actCriticAlgorithm; @SusanMurphy_2016_CORR_BatchOffPolicyAvgRwd; @PengLiao_2015_Proposal_offPolicyRL; @yaoyao_2017_MICCAI; @xinliang_2017_CVPR_WSISA; @zhengxu_2017_ACMBCB]. In this paper, the goal of mobile health (mHealth) is to make use of various smart devices as great platforms to 