… , each device i decides whether or not to participate to train its … variable of device participation,
where ui = 1 means device i … After finishing the device participation, the devices feedback …

… Reinforcement learning (RL) is machine learning in which machines interact with environments
and perform self- learning. … , all devices may not be able to participate in learning on all …

… 2) On the basis of the set of candidate edge devices, we determine the final edge devices
participating in training in line with the perception of social attributes, which ensures the …

… platform and smart device users. Once mobile users are participating in MCS programs, …
Yet to solve multi-agent MDPs with non-stationary participants’ sensing environment aided …

… -moving users (called participants or workers) contribute locationdependent urban sensing
information through various types of sensors embedded in their mobile devices. By collecting …

… The monitoring device can identify participants who can benefit from a physical activity
monitoring system once positive results. Integrated cloud computing and the IoT system for health …

… Taking into account the heterogeneity of devices held by participants, we set ωm … can be
solved by reinforcement learning. For participants, we propose to use Q-learning to obtain the …

… device participating in a continuous intra-day (CID) market is addressed in this paper.
The goal of the storage device … The problem is solved using deep reinforcement learning (RL). …

… of 60 participants for improving … a reinforcement learning based algorithm to improve
notification response rate, which in turn increases the quality of interactions from mobile devices …

… of mobile devices, crowdsourcing has become a new service paradigm in which a task
requester can proactively recruit a batch of participants with a mobile IoT device from our system …