Implications of decentralized Q-learning resource allocation in wireless networks
Reinforcement Learning is gaining attention by the wireless networking community due to its
potential to learn good-performing configurations only from the observed results. In this work …
potential to learn good-performing configurations only from the observed results. In this work …
Distributed Reinforcement Learning for scalable wireless medium access in IoTs and sensor networks
This paper presents a distributed Reinforcement Learning (RL) framework for synthesizing
wireless network protocols in IoT and Wireless Sensor Networks with low-complexity …
wireless network protocols in IoT and Wireless Sensor Networks with low-complexity …
Reinforcement learning meets wireless networks: A layering perspective
Driven by the soaring traffic demand and the growing diversity of mobile services, wireless
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …
Q-learning with experience replay in a dynamic environment
M Pieters, MA Wiering - 2016 IEEE Symposium Series on …, 2016 - ieeexplore.ieee.org
Most research in reinforcement learning has focused on stationary environments. In this
paper, we propose several adaptations of Q-learning for a dynamic environment, for both …
paper, we propose several adaptations of Q-learning for a dynamic environment, for both …
Collaborative spatial reuse in wireless networks via selfish multi-armed bandits
Next-generation wireless deployments are characterized by being dense and
uncoordinated, which often leads to inefficient use of resources and poor performance. To …
uncoordinated, which often leads to inefficient use of resources and poor performance. To …
Cooperative multi-agent reinforcement learning for low-level wireless communication
Traditional radio systems are strictly co-designed on the lower levels of the OSI stack for
compatibility and efficiency. Although this has enabled the success of radio communications …
compatibility and efficiency. Although this has enabled the success of radio communications …
From cognition to docition: The teaching radio paradigm for distributed & autonomous deployments
L Giupponi, AM Galindo-Serrano, M Dohler - Computer Communications, 2010 - Elsevier
We advocate for a novel communication paradigm of docition which facilitates distributed
and autonomous networking at minimal control overhead and maximal performance. We …
and autonomous networking at minimal control overhead and maximal performance. We …
Conservative q-learning for offline reinforcement learning
Effectively leveraging large, previously collected datasets in reinforcement learn-ing (RL) is
a key challenge for large-scale real-world applications. Offline RL algorithms promise to …
a key challenge for large-scale real-world applications. Offline RL algorithms promise to …
Deep-reinforcement learning multiple access for heterogeneous wireless networks
This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple …
Marconi-rosenblatt framework for intelligent networks (mr-inet gym): For rapid design and implementation of distributed multi-agent reinforcement learning solutions for …
Abstract We present the Marconi-Rosenblatt Framework for Intelligent Networks (MR-iNet
Gym) an open-source architecture designed for accelerating research and development of …
Gym) an open-source architecture designed for accelerating research and development of …