A review of safe reinforcement learning: Methods, theory and applications
Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …
making tasks. However, safety concerns are raised during deploying RL in real-world …
[PDF][PDF] Policy learning with constraints in model-free reinforcement learning: A survey
Reinforcement Learning (RL) algorithms have had tremendous success in simulated
domains. These algorithms, however, often cannot be directly applied to physical systems …
domains. These algorithms, however, often cannot be directly applied to physical systems …
Deep reinforcement learning for adaptive network slicing in 5G for intelligent vehicular systems and smart cities
A Nassar, Y Yilmaz - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
Intelligent vehicular systems and smart city applications are the fastest growing Internet-of-
Things (IoT) implementations at a compound annual growth rate of 30%. In view of the …
Things (IoT) implementations at a compound annual growth rate of 30%. In view of the …
Intelligent joint network slicing and routing via GCN-powered multi-task deep reinforcement learning
In 6G mobile systems, network slicing is an emerging technology to support services with
distinct requirements by dividing a common infrastructure into multiple logical networks …
distinct requirements by dividing a common infrastructure into multiple logical networks …
Applications of machine learning in resource management for RAN-slicing in 5G and beyond networks: A survey
One of the key foundations of 5th Generation (5G) and beyond 5G (B5G) networks is
network slicing, in which the network is partitioned into several separated logical networks …
network slicing, in which the network is partitioned into several separated logical networks …
Machine learning in network slicing—a survey
5G and beyond networks are expected to support a wide range of services, with highly
diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the …
diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the …
Deep reinforcement learning approaches to network slice scaling and placement: A survey
N Saha, M Zangooei, M Golkarifard… - IEEE Communications …, 2023 - ieeexplore.ieee.org
Network slicing in 5G and beyond networks allows the network to be customized for each
application or service by chaining together different virtualized network functions (VNFs) …
application or service by chaining together different virtualized network functions (VNFs) …
Reinforcement learning for radio resource management in ran slicing: A survey
M Zangooei, N Saha, M Golkarifard… - IEEE Communications …, 2023 - ieeexplore.ieee.org
Dynamic radio resource allocation to network slices in mobile networks is challenging due to
the diverse requirements of RAN slices and the dynamic environment of wireless networks …
the diverse requirements of RAN slices and the dynamic environment of wireless networks …
OnSlicing: online end-to-end network slicing with reinforcement learning
Network slicing allows mobile network operators to virtualize infrastructures and provide
customized slices for supporting various use cases with heterogeneous requirements …
customized slices for supporting various use cases with heterogeneous requirements …
Flexible RAN Slicing in Open RAN With Constrained Multi-Agent Reinforcement Learning
M Zangooei, M Golkarifard, M Rouili… - IEEE Journal on …, 2023 - ieeexplore.ieee.org
Network slicing enables the provision of customized services in next-generation mobile
networks. Accordingly, the network is divided into logically isolated networks that share …
networks. Accordingly, the network is divided into logically isolated networks that share …