Airborne Multi-platform Sensor Scheduling Based on Reinforcement Learning

Mapping Intimacies ◽

10.1007/978-981-15-8155-7_172 ◽

2021 ◽

pp. 2049-2059

Author(s):

Yuedong Wang ◽

Jing He ◽

Shi Yan ◽

Yan Liang

Keyword(s):

Reinforcement Learning ◽

Sensor Scheduling

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Optimal sensor scheduling for remote state estimation with limited bandwidth: a deep reinforcement learning approach

Information Sciences ◽

10.1016/j.ins.2021.12.043 ◽

2021 ◽

Author(s):

Lixin Yang ◽

Hongxia Rao ◽

Ming Lin ◽

Yong Xu ◽

Peng Shi

Keyword(s):

Reinforcement Learning ◽

State Estimation ◽

Learning Approach ◽

Sensor Scheduling ◽

Limited Bandwidth ◽

Remote State Estimation

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Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems

10.1016/j.automatica.2019.108759 ◽

2020 ◽

Vol 113 ◽

pp. 108759 ◽

Author(s):

Alex S. Leong ◽

Arunselvan Ramaswamy ◽

Daniel E. Quevedo ◽

Holger Karl ◽

Ling Shi

Keyword(s):

Reinforcement Learning ◽

Cyber Physical Systems ◽

Wireless Sensor ◽

Sensor Scheduling ◽

Physical Systems

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Confirmation sensor scheduling using a reinforcement learning approach

10.1117/12.665900 ◽

2006 ◽

Author(s):

Jay A. Marble ◽

Doron Blatt ◽

Alfred O. Hero

Keyword(s):

Reinforcement Learning ◽

Learning Approach ◽

Sensor Scheduling

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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 ◽

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 ◽

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 ◽

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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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A Collaborative Scheduling Lane Changing Model for Intelligent Connected Vehicles Based on Deep Reinforcement Learning

10.1061/9780784483053.178 ◽

2020 ◽

Author(s):

Zheyu Cui ◽

Jianming Hu

Keyword(s):

Reinforcement Learning ◽

Connected Vehicles ◽

Lane Changing ◽

Collaborative Scheduling

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