Impact: Importance weighted asynchronous architectures with clipped target networks
The practical usage of reinforcement learning agents is often bottlenecked by the duration of
training time. To accelerate training, practitioners often turn to distributed reinforcement …
training time. To accelerate training, practitioners often turn to distributed reinforcement …
Online learning in autonomic multi-hop wireless networks for transmitting mission-critical applications
HP Shiang, M van der Schaar - IEEE Journal on Selected Areas …, 2010 - ieeexplore.ieee.org
In this paper, we study how to optimize the transmission decisions of nodes aimed at
supporting mission-critical applications, such as surveillance, security monitoring, and …
supporting mission-critical applications, such as surveillance, security monitoring, and …
Learning to Fly: A Distributed Deep Reinforcement Learning Framework for Software-Defined UAV Network Control
Control and performance optimization of wireless networks of Unmanned Aerial Vehicles
(UAVs) require scalable approaches that go beyond architectures based on centralized …
(UAVs) require scalable approaches that go beyond architectures based on centralized …
A federated reinforcement learning framework for incumbent technologies in beyond 5G networks
Incumbent wireless technologies for futuristic fifth generation (5G) and beyond 5G (B5G)
networks, such as IEEE 802.11 ax (WiFi), are vital to provide ubiquitous ultra-reliable and …
networks, such as IEEE 802.11 ax (WiFi), are vital to provide ubiquitous ultra-reliable and …
Applications of Deep Reinforcement Learning in Wireless Networks-A Recent Review
Deep Reinforcement Learning (DRL) techniques have gained substantial attention in recent
years for future wireless networks. They can overcome the ever-increasing challenges of …
years for future wireless networks. They can overcome the ever-increasing challenges of …
Federated deep reinforcement learning for the distributed control of NextG wireless networks
P Tehrani, F Restuccia… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Next Generation (NextG) networks are expected to support demanding tactile internet
applications such as augmented reality and connected autonomous vehicles. Whereas …
applications such as augmented reality and connected autonomous vehicles. Whereas …
Learning to schedule communication in multi-agent reinforcement learning
Many real-world reinforcement learning tasks require multiple agents to make sequential
decisions under the agents' interaction, where well-coordinated actions among the agents …
decisions under the agents' interaction, where well-coordinated actions among the agents …
Kogun: accelerating deep reinforcement learning via integrating human suboptimal knowledge
Reinforcement learning agents usually learn from scratch, which requires a large number of
interactions with the environment. This is quite different from the learning process of human …
interactions with the environment. This is quite different from the learning process of human …
Federated reinforcement learning for training control policies on multiple IoT devices
Reinforcement learning has recently been studied in various fields and also used to
optimally control IoT devices supporting the expansion of Internet connection beyond the …
optimally control IoT devices supporting the expansion of Internet connection beyond the …
[PDF][PDF] A survey of reinforcement learning techniques: strategies, recent development, and future directions
AK Mondal, N Jamali - arXiv preprint arXiv:2001.06921, 2020 - researchgate.net
Reinforcement learning is one of the core components in designing an artificial intelligent
system emphasizing real-time response. Reinforcement learning influences the system to …
system emphasizing real-time response. Reinforcement learning influences the system to …