Deep reinforcement learning based scheduling within production plan in semiconductor fabrication

Expert Systems with Applications ◽

10.1016/j.eswa.2021.116222 ◽

2021 ◽

pp. 116222

Author(s):

Young Hoon Lee ◽

Seunghoon Lee

Keyword(s):

Reinforcement Learning ◽

Production Plan ◽

Semiconductor Fabrication

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Idle Vehicle Rebalancing in Semiconductor Fabrication Using Factorized Graph Neural Network Reinforcement Learning

2019 IEEE 58th Conference on Decision and Control (CDC) ◽

10.1109/cdc40024.2019.9030245 ◽

2019 ◽

Author(s):

Kyuree Ahn ◽

Jinkyoo Park

Keyword(s):

Neural Network ◽

Reinforcement Learning ◽

Semiconductor Fabrication

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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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Multi-Agent Deep Reinforcement Learning for Decentralized Cooperative Traffic Signal Control

10.1061/9780784483053.039 ◽

2020 ◽

Author(s):

Yang Zhao ◽

Jian-Ming Hu ◽

Ming-Yang Gao ◽

Zuo Zhang

Keyword(s):

Reinforcement Learning ◽

Traffic Signal ◽

Signal Control ◽

Traffic Signal Control ◽

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Research on Signal Control Method of Single Intersection Based on Reinforcement Learning

10.1061/9780784483053.015 ◽

2020 ◽

Author(s):

Yilong Ren ◽

Le Zhang ◽

Han Jiang ◽

Chengsheng Liu

Keyword(s):

Reinforcement Learning ◽

Control Method ◽

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