[HTML][HTML] A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization

C Ssengonzi, OP Kogeda, TO Olwal - Array, 2022 - Elsevier
Abstract The 5th Generation (5G) and beyond networks are expected to offer huge
throughputs, connect large number of devices, support low latency and large numbers of …

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

W Zhang, R Chen, J Li, T Huang, B Wu, J Ma, Q Wen… - Biochar, 2023 - Springer
Due to large specific surface area, abundant functional groups and low cost, biochar is
widely used for pollutant removal. The adsorption performance of biochar is related to …

[HTML][HTML] Industrial cyber-physical systems protection: A methodological review

R Canonico, G Sperlì - Computers & Security, 2023 - Elsevier
Ubiquitous utilization of Information and Communication Technologies in modern
manufacturing plants has transformed them into Cyber-Physical Systems (CPSs), making …

Reinforcement learning-based routing protocols in flying ad hoc networks (FANET): A review

J Lansky, S Ali, AM Rahmani, MS Yousefpoor… - Mathematics, 2022 - mdpi.com
In recent years, flying ad hoc networks have attracted the attention of many researchers in
industry and universities due to easy deployment, proper operational costs, and diverse …

Factored adaptation for non-stationary reinforcement learning

F Feng, B Huang, K Zhang… - Advances in Neural …, 2022 - proceedings.neurips.cc
Dealing with non-stationarity in environments (eg, in the transition dynamics) and objectives
(eg, in the reward functions) is a challenging problem that is crucial in real-world …

Deep learning models for serendipity recommendations: a survey and new perspectives

Z Fu, X Niu, ML Maher - ACM Computing Surveys, 2023 - dl.acm.org
Serendipitous recommendations have emerged as a compelling approach to deliver users
with unexpected yet valuable information, contributing to heightened user satisfaction and …

Near-optimal model-free reinforcement learning in non-stationary episodic mdps

W Mao, K Zhang, R Zhu… - … on Machine Learning, 2021 - proceedings.mlr.press
We consider model-free reinforcement learning (RL) in non-stationary Markov decision
processes. Both the reward functions and the state transition functions are allowed to vary …

Deep reinforcement learning for anomaly detection: A systematic review

K Arshad, RF Ali, A Muneer, IA Aziz, S Naseer… - IEEE …, 2022 - ieeexplore.ieee.org
Anomaly detection has been used to detect and analyze anomalous elements from data for
years. Various techniques have been developed to detect anomalies. However, the most …

Universal off-policy evaluation

Y Chandak, S Niekum, B da Silva… - Advances in …, 2021 - proceedings.neurips.cc
When faced with sequential decision-making problems, it is often useful to be able to predict
what would happen if decisions were made using a new policy. Those predictions must …

Reinforcement learning-based routing protocols in vehicular ad hoc networks for intelligent transport system (its): A survey

J Lansky, AM Rahmani, M Hosseinzadeh - Mathematics, 2022 - mdpi.com
Today, the use of safety solutions in Intelligent Transportation Systems (ITS) is a serious
challenge because of novel progress in wireless technologies and the high number of road …