A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution
AH Ganesh, B Xu - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
The impact of internal combustion engine-powered automobiles on climate change due to
emissions and the depletion of fossil fuels has contributed to the progress of electrified …
emissions and the depletion of fossil fuels has contributed to the progress of electrified …
Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …
led to multiple successes in solving sequential decision-making problems in various …
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 …
Edge intelligence for energy-efficient computation offloading and resource allocation in 5G beyond
Y Dai, K Zhang, S Maharjan… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous
capabilities of the end devices, edge servers, and the cloud and thus has the potential to …
capabilities of the end devices, edge servers, and the cloud and thus has the potential to …
Deep reinforcement learning for autonomous internet of things: Model, applications and challenges
The Internet of Things (IoT) extends the Internet connectivity into billions of IoT devices
around the world, where the IoT devices collect and share information to reflect status of the …
around the world, where the IoT devices collect and share information to reflect status of the …
Computation offloading optimization for UAV-assisted mobile edge computing: a deep deterministic policy gradient approach
Abstract Unmanned Aerial Vehicle (UAV) can play an important role in wireless systems as it
can be deployed flexibly to help improve coverage and quality of communication. In this …
can be deployed flexibly to help improve coverage and quality of communication. In this …
Reinforcement learning based energy management systems and hydrogen refuelling stations for fuel cell electric vehicles: An overview
R Venkatasatish, C Dhanamjayulu - International Journal of Hydrogen …, 2022 - Elsevier
This paper examines the current state of the art of hydrogen refuelling stations-based
production and storage systems for fuel cell hybrid electric vehicles (FCHEV). Nowadays …
production and storage systems for fuel cell hybrid electric vehicles (FCHEV). Nowadays …
Deep reinforcement learning based computation offloading and trajectory planning for multi-UAV cooperative target search
Unmanned aerial vehicles (UAVs) are widely used for surveillance and monitoring to
complete target search tasks. However, the short battery life and moderate computational …
complete target search tasks. However, the short battery life and moderate computational …
Model-free emergency frequency control based on reinforcement learning
Unexpected large power surges will cause instantaneous grid shock and, thus, emergency
control plans must be implemented to prevent the system from collapsing. In this article, with …
control plans must be implemented to prevent the system from collapsing. In this article, with …
AI empowered RIS-assisted NOMA networks: Deep learning or reinforcement learning?
A reconfigurable intelligent surface (RIS)-assisted multi-user downlink communication
system over fading channels is investigated, where both non-orthogonal multiple access …
system over fading channels is investigated, where both non-orthogonal multiple access …