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Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing
[article]
2021
arXiv
pre-print
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In this paper, a novel bandwidth negotiation mechanism is proposed for massive devices wireless spectrum sharing, in which individual device locally negotiates bandwidth usage with neighbor devices and ...
In order to solve the distributed optimization problem when massive devices coexist, mean field multi-agent reinforcement learning (MF-MARL) based bandwidth decision algorithm is proposed, which allow ...
MARL is a combination of reinforcement learning and game theory, which consists of an environment and multiple agents gaming each other. ...
arXiv:2104.15085v1
fatcat:we65dwuhxvfopfpuvgxofvc2oe
Emergent Communication through Negotiation
[article]
2018
arXiv
pre-print
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Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. ...
We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation. ...
ACKNOWLEDGEMENTS We would like to thank Mike Johanson for his insightful comments on an earlier version of this paper, as well as Karl Moritz Hermann and the rest of the DeepMind language team for many ...
arXiv:1804.03980v1
fatcat:wnc5qyoyx5cq3mgvyenirbfloe
Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning
[article]
2018
arXiv
pre-print
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A simulative evaluation of reinforcement learning agents based on neural networks, called deep Q-networks and double deep Q-networks, was realized for exemplary and practically relevant coexistence scenarios ...
This paper presents a self-learning concept, which is based on reinforcement learning. ...
Section III will explain the concept of reinforcement learning based resource allocation for a central coexistence management system. ...
arXiv:1806.04702v1
fatcat:rqytmp3szzb2le3vjaoymwga6m
Reinforcement Learning-Based Television White Space Database
2021
Baghdad Science Journal
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With this in mind, the authors present a reinforcement learning-based TVWSDB. ...
This paper presents the advantage of using a machine learning approach in TVWSDB with an accurate and faster-searching capability for the available TVWS channels intended for SUs. ...
Acknowledgment: The authors acknowledge De La Salle University for its support in the publication of this paper. ...
doi:10.21123/bsj.2021.18.2(suppl.).0947
fatcat:6jjuneuwabh7png6vxu3m7lnz4
Network resource optimization with reinforcement learning for low power wide area networks
2020
EURASIP Journal on Wireless Communications and Networking
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With deep reinforcement learning, we address the LoRaWAN problem with the goal of optimizing the distribution of network resources such as spreading factor, transmission power, and channel. ...
By creating a number of deep reinforcement learning agents that match the terminal nodes in the network server, the optimal transmission parameters are provided to the terminal nodes. ...
Deep reinforcement learning Reinforcement learning is an algorithm that derives the best value for the situation through interaction with the environment. ...
doi:10.1186/s13638-020-01783-5
fatcat:odrrqgx6dfe4pbwxrjoan6uoqu
Traffic Prediction and Random Access Control Optimization: Learning and Non-learning based Approaches
[article]
2020
arXiv
pre-print
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control configuration can use either a non-ML based controller or a cooperatively trained Deep Reinforcement Learning (DRL) based controller depending on the complexity of different random access schemes ...
In this article, we first summarize the general structure of access control optimization for different random access schemes, and then review the existing access control optimization based on Machine Learning ...
control configuration can use either a non-ML based controller or a cooperatively trained Deep Reinforcement Learning (DRL) based controller depending on the complexity of different random access schemes ...
arXiv:2002.07759v1
fatcat:vwkevr2icnhwdpane7upifvuqe
Emergent Multi-Agent Communication in the Deep Learning Era
[article]
2020
arXiv
pre-print
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From a scientific perspective, understanding the conditions under which language evolves in communities of deep agents and its emergent features can shed light on human language evolution. ...
From an applied perspective, endowing deep networks with the ability to solve problems interactively by communicating with each other and with us should make them more flexible and useful in everyday life ...
Diane Bouchacourt and Emmanuel Dupoux for useful discussions. ...
arXiv:2006.02419v2
fatcat:rk7rmdewdfcxrgj4llrqww7cpi
Learning to Communicate in Multi-Agent Reinforcement Learning : A Review
[article]
2019
arXiv
pre-print
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We consider the issue of multiple agents learning to communicate through reinforcement learning within partially observable environments, with a focus on information asymmetry in the second part of our ...
We provide a review of the recent algorithms developed to improve the agents' policy by allowing the sharing of information between agents and the learning of communication strategies, with a focus on ...
Introduction In recent years, Multi-Agent Reinforcement Learning has received a lot of interest, with more and more complex algorithms and structures improving the policy of multiple agents in more and ...
arXiv:1911.05438v1
fatcat:2rr4mo7afjdt3hvf445ktn6lxu
Towards Learning Multi-agent Negotiations via Self-Play
[article]
2020
arXiv
pre-print
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In contrast, deep reinforcement learning (Deep RL) has been very effective at finding policies by simultaneously exploring, interacting, and learning from environments. ...
We demonstrate this in a challenging multi-agent simulation of merging traffic, where agents must interact and negotiate with others in order to successfully merge on or off the road. ...
Acknowledgements We thank Barry Theobald, Hanlin Goh, Ruslan Salakhutdinov, Jian Zhang, Nitish Srivastava, Alex Druinsky, and the anonymous reviewers for making this a better manuscript. ...
arXiv:2001.10208v1
fatcat:dp4ucamn5bektnd36ombdmxyfy
Towards Learning Multi-Agent Negotiations via Self-Play
2019
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
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In contrast, deep reinforcement learning (Deep RL) has been very effective at finding policies by simultaneously exploring, interacting, and learning from environments. ...
We demonstrate this in a challenging multi-agent simulation of merging traffic, where agents must interact and negotiate with others in order to successfully merge on or off the road. ...
Acknowledgements We thank Barry Theobald, Hanlin Goh, Ruslan Salakhutdinov, Jian Zhang, Nitish Srivastava, Alex Druinsky, and the anonymous reviewers for making this a better manuscript. ...
doi:10.1109/iccvw.2019.00297
dblp:conf/iccvw/Tang19
fatcat:5vyk4zkwcrdozmk3cfn75ttzpu
On the Performance of Deep Reinforcement Learning-Based Anti-Jamming Method Confronting Intelligent Jammer
2019
Applied Sciences
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With the development of access technologies and artificial intelligence, a deep reinforcement learning (DRL) algorithm is proposed into channel accessing and anti-jamming. ...
First of all, we design an intelligent jamming method based on reinforcement learning to combat the DRL-based user. ...
In future work, we will study the situations where a user can work on multiple channels simultaneously. Figure 1 . 1 System model.
Figure 2 . 2 Mechanism of reinforcement learning. ...
doi:10.3390/app9071361
fatcat:ak7jto3m7vddjikmxpweu46zge
The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning
[article]
2021
arXiv
pre-print
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In this paper, we propose a new framework, exploiting the multi-agent deep deterministic policy gradient (MADDPG) algorithm, to enable a base station (BS) and user equipment (UE) to come up with a medium ...
In such a framework, the agents have to learn not only the channel access policy, but also the signaling policy. ...
MARL is an extension of RL for to multi-agent systems (MAS), where multiple agents interact with a system, i.e the environment. ...
arXiv:2108.07144v2
fatcat:bn4lbvtf7jbebnnuvcuzdjjjha
Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
2021
Sensors
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To solve this problem, we establish an optimization problem and employ a reinforcement-learning-based scheme. ...
Through simulation, we demonstrate that our proposed deep learning-based algorithm improves performance on random access. ...
In the recent advancement in the field of AI, deep reinforcement learning is proposed where deep learning is combined with reinforcement learning. ...
doi:10.3390/s21093210
pmid:34063132
fatcat:qr5uur4d3zbnriw4qg32pgg2l4
A Kind of Joint Routing and Resource Allocation Scheme Based on Prioritized Memories-Deep Q Network for Cognitive Radio Ad Hoc Networks
2018
Sensors
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In this paper, a deep reinforcement learning approach is adopted for solving above problem. ...
One of the critical challenges for setting up such systems is how to coordinate multiple protocol layers such as routing and spectrum access in a partially observable environment. ...
Acknowledgments: The authors wish to thank the editor and anonymous referees for their helpful comments in improving the quality of this paper. ...
doi:10.3390/s18072119
pmid:30004424
pmcid:PMC6068577
fatcat:yo3excumnba5bmy2ieawuipcgy
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
[article]
2018
arXiv
pre-print
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Intelligent wireless communications aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure ...
In this paper, we discuss the development of the cognitive radio technology and machine learning techniques and emphasize their roles in improving spectrum and energy utility of wireless communication ...
A channel selection strategy based on multi-agent reinforcement learning is proposed in multi-user and multi-channel cognitive radio systems for secondary users to avoid the negotiation overhead [149] ...
arXiv:1710.11240v4
fatcat:elt77cgcxvappbxvspp7evb74u
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