social information. The given network may not be targeted for the mHealth study. There is lots of misleading network information for the mHealth study [@Bianchi_2013_NIPS_GangOfBandit; @Claudio_2014_ICML_olineClusteringBandits; @Alexandra_2016_AISTATS_GraphBandits]. (3) In their work, however, it is unable to control the amount of information shared among linked users, which is not flexible for the mHealth study [@fyzhu_2014_IJPRS_SSNMF; @haichangLi_2016_IJRS_LablePropagationHyperClassification].

In this paper, we propose a cohesion-based reinforcement learning for the mHealth and derive two algorithms. It is in an online, actor-critic setting. The aim is to explore how to share information across similar users in order to improve the performance. The main contributions of this paper are summarized as follows: (**1**) to the best of our knowledge, this is the first online (actor-critic) RL method for the mHealth. (**2**) Current evidence verifies the wide existence of networks among users [@Tianxi_2016_CORR_PredictModels4NetworkLinkedData; @fyzhu_2014_IJPRS_SSNMF; @haichangLi_2016_IJRS_LablePropagationHyperClassification]. We improve the online RL by considering the network cohesion among users. Such improvement makes it the first network constrained (actor-critic) RL method to the best of our knowledge. It is able to relieve the tough problem of current online decision-making methods for the mHealth by reducing variance at the cost of inducing bias. Current online RL learns a separate policy for 