Adaptive wireless network management with multi-agent reinforcement learning
Wireless networks are trending towards large scale systems, containing thousands of nodes,
with multiple co-existing applications. Congestion is an inevitable consequence of this scale …
with multiple co-existing applications. Congestion is an inevitable consequence of this scale …
GrGym: When GNU radio goes to (AI) gym
A Zubow, S Rösler, P Gawłowicz… - Proceedings of the 22nd …, 2021 - dl.acm.org
Trends like softwarization through the usage of flexible Software-defined Radio (SDR)
platforms together with the usage of Machine Learning (ML) techniques are key enablers for …
platforms together with the usage of Machine Learning (ML) techniques are key enablers for …
Enhanced off-policy reinforcement learning with focused experience replay
SH Kong, IMA Nahrendra, DH Paek - IEEE Access, 2021 - ieeexplore.ieee.org
Utilizing the collected experience tuples in the replay buffer (RB) is the primary way of
exploiting the experiences in the off-policy reinforcement learning (RL) algorithms, and …
exploiting the experiences in the off-policy reinforcement learning (RL) algorithms, and …
Scalable reinforcement learning for multiagent networked systems
We study reinforcement learning (RL) in a setting with a network of agents whose states and
actions interact in a local manner where the objective is to find localized policies such that …
actions interact in a local manner where the objective is to find localized policies such that …
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …
beginning to show some successes in real-world scenarios. However, much of the research …
ns3-gym: Extending openai gym for networking research
P Gawłowicz, A Zubow - arXiv preprint arXiv:1810.03943, 2018 - arxiv.org
OpenAI Gym is a toolkit for reinforcement learning (RL) research. It includes a large number
of well-known problems that expose a common interface allowing to directly compare the …
of well-known problems that expose a common interface allowing to directly compare the …
Deep reinforcement learning for multiple agents in a decentralized architecture: a case study in the telecommunication domain
H Zhang, J Li, Z Qi, A Aronsson… - 2023 IEEE 20th …, 2023 - ieeexplore.ieee.org
Deep reinforcement learning has made significant development in recent years, and it is
currently applied not only in simulators and games but also in embedded systems. However …
currently applied not only in simulators and games but also in embedded systems. However …
Learning driven mobility control of airborne base stations in emergency networks
Mobile base stations mounted on unmanned aerial vehicles (UAVs) provide viable wireless
coverage solutions in challenging landscapes and conditions, where cellular/WiFi …
coverage solutions in challenging landscapes and conditions, where cellular/WiFi …
Federated learning and control at the wireless network edge
M Bennis - GetMobile: Mobile Computing and Communications, 2021 - dl.acm.org
We are at the cusp of two transformational technologies, namely the fifth generation of
wireless communication systems, known as 5G, and machine learning (ML). On the one …
wireless communication systems, known as 5G, and machine learning (ML). On the one …
Maca: a multi-agent reinforcement learning platform for collective intelligence
F Gao, S Chen, M Li, B Huang - 2019 IEEE 10th International …, 2019 - ieeexplore.ieee.org
Heterogeneous multi-agent cooperative decisionmaking is one of the kernel problems in
collective intelligence field. Reinforcement learning may be an effective technology to …
collective intelligence field. Reinforcement learning may be an effective technology to …