Reinforcement Learning for Scheduling and Mimo beam Selection using Caviar Simulations

Mapping Intimacies ◽

10.23919/ituk53220.2021.9662100 ◽

2021 ◽

Author(s):

Joao Paulo Tavares Borges ◽

Ailton Pinto De Oliveira ◽

Felipe Henrique Bastos E Bastos ◽

Daniel Takashi Ne Do Nascimento Suzuki ◽

Emerson Santos De Oliveira Junior ◽

...

Keyword(s):

Reinforcement Learning ◽

Beam Selection

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Coordinated beam selection in LTE-Advanced HetNets: A reinforcement learning approach

2012 IEEE Globecom Workshops ◽

10.1109/glocomw.2012.6477642 ◽

2012 ◽

Cited By ~ 6

Author(s):

Meryem Simsek ◽

Mehdi Bennis ◽

Andreas Czylwik

Keyword(s):

Reinforcement Learning ◽

Learning Approach ◽

Lte Advanced ◽

Beam Selection

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Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays

IEEE Journal on Selected Areas in Communications ◽

10.1109/jsac.2021.3087233 ◽

2021 ◽

pp. 1-1

Author(s):

Qiyu Hu ◽

Yanzhen Liu ◽

Yunlong Cai ◽

Guanding Yu ◽

Zhi Ding

Keyword(s):

Reinforcement Learning ◽

Multiuser Mimo ◽

Beam Selection

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Multi-Agent Reinforcement Learning-Based Cooperative Beam Selection in mmWave Vehicular Networks

10.1109/mass52906.2021.00069 ◽

2021 ◽

Author(s):

Lei Wang ◽

Shuxin Ge ◽

Xiaobo Zhou ◽

Tie Qiu ◽

Keqiu Li

Keyword(s):

Reinforcement Learning ◽

Vehicular Networks ◽

Beam Selection ◽

Multi Agent

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Joint User Scheduling and Beam Selection in mmWave Networks Based on Multi-Agent Reinforcement Learning

2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM) ◽

10.1109/sam48682.2020.9104386 ◽

2020 ◽

Author(s):

Chunmei Xu ◽

Shengheng Liu ◽

Cheng Zhang ◽

Yongming Huang ◽

Luxi Yang

Keyword(s):

Reinforcement Learning ◽

User Scheduling ◽

Beam Selection ◽

Multi Agent

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Supplemental Material for Reconciling Reinforcement Learning Models With Behavioral Extinction and Renewal: Implications for Addiction, Relapse, and Problem Gambling

Psychological Review ◽

10.1037/0033-295x.114.3.784.supp ◽

2007 ◽

Cited By ~ 1

Keyword(s):

Reinforcement Learning ◽

Problem Gambling ◽

Learning Models ◽

Behavioral Extinction ◽

Reinforcement Learning Models

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Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽

10.1037/dec0000040 ◽

2016 ◽

Vol 3 (2) ◽

pp. 115-131 ◽

Cited By ~ 14

Author(s):

Helen Steingroever ◽

Ruud Wetzels ◽

Eric-Jan Wagenmakers

Keyword(s):

Reinforcement Learning ◽

Iowa Gambling Task ◽

Bayes Factors ◽

Gambling Task ◽

Learning Models ◽

Reinforcement Learning Models

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Analogical Reinforcement Learning With Two-Stage Memory Retrieval

PsycEXTRA Dataset ◽

10.1037/e528942014-705 ◽

2014 ◽

Author(s):

James Foster ◽

Matt Jones

Keyword(s):

Reinforcement Learning ◽

Memory Retrieval ◽

Two Stage

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Effects of Working Memory Capacity on the Speed and Accuracy of Learning in Reinforcement Learning Models

PsycEXTRA Dataset ◽

10.1037/e528942014-552 ◽

2014 ◽

Author(s):

Adnane Ez-Zizi ◽

Simon Farrell ◽

David Leslie

Keyword(s):

Working Memory ◽

Reinforcement Learning ◽

Working Memory Capacity ◽

Memory Capacity ◽

Learning Models ◽

Reinforcement Learning Models ◽

Speed And Accuracy

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Supplemental Material for Reinforcement Learning Models of Risky Choice and the Promotion of Risk-Taking by Losses Disguised as Wins in Rats

Journal of Experimental Psychology Animal Learning and Cognition ◽

10.1037/xan0000141.supp ◽

2017 ◽

Keyword(s):

Reinforcement Learning ◽

Risk Taking ◽

Risky Choice ◽

Learning Models ◽

Losses Disguised As Wins ◽

Reinforcement Learning Models

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Reinforcement learning of irrelevant stimulus-response associations modulates cognitive control.

Journal of Experimental Psychology Learning Memory and Cognition ◽

10.1037/xlm0000850 ◽

2020 ◽

Author(s):

Jinglu Chen ◽

Ling Tan ◽

Lu Liu ◽

Ling Wang

Keyword(s):

Reinforcement Learning ◽

Cognitive Control ◽

Irrelevant Stimulus ◽

Stimulus Response

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