Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
From cloud down to things: An overview of machine learning in internet of things
With the numerous Internet of Things (IoT) devices, the cloud-centric data processing fails to
meet the requirement of all IoT applications. The limited computation and communication …
meet the requirement of all IoT applications. The limited computation and communication …
Learning optimal resource allocations in wireless systems
This paper considers the design of optimal resource allocation policies in wireless
communication systems, which are generically modeled as a functional optimization …
communication systems, which are generically modeled as a functional optimization …
Event-triggered communication network with limited-bandwidth constraint for multi-agent reinforcement learning
Communicating agents with each other in a distributed manner and behaving as a group are
essential in multi-agent reinforcement learning. However, real-world multi-agent systems …
essential in multi-agent reinforcement learning. However, real-world multi-agent systems …
Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems
In many cyber–physical systems, we encounter the problem of remote state estimation of
geographically distributed and remote physical processes. This paper studies the …
geographically distributed and remote physical processes. This paper studies the …
On the latency, rate, and reliability tradeoff in wireless networked control systems for IIoT
Wireless networked control systems (WNCSs) provide a key enabling technique for
Industrial Internet of Things (IIoT). However, in the literature of WNCSs, most of the research …
Industrial Internet of Things (IIoT). However, in the literature of WNCSs, most of the research …
Hybrid reinforcement learning control for a micro quadrotor flight
This letter presents a combination of reinforcement learning (RL) and deterministic
controllers to learn a quadrotor control. Learning the quadrotor flight in a standard RL …
controllers to learn a quadrotor control. Learning the quadrotor flight in a standard RL …
DDQN-TS: A novel bi-objective intelligent scheduling algorithm in the cloud environment
Z Tong, F Ye, B Liu, J Cai, J Mei - Neurocomputing, 2021 - Elsevier
Task scheduling has always been one of the crucial problem in cloud computing. With the
transition of task types from static batch processing to dynamic stream processing, the …
transition of task types from static batch processing to dynamic stream processing, the …
Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport
K Shibata, T Jimbo, T Matsubara - Robotics and Autonomous Systems, 2023 - Elsevier
In this paper, we present a solution to a design problem of control strategies for multi-agent
cooperative transport. Although existing learning-based methods assume that the number of …
cooperative transport. Although existing learning-based methods assume that the number of …
Reinforcement learning for cyber-physical systems
Cyber-Physical Systems (CPS), including smart industrial manufacturing, smart
transportation, and smart grids, among others, are envisioned to convert traditionally …
transportation, and smart grids, among others, are envisioned to convert traditionally …